('featured_article',0,74,630,'en',0,'<p>Artificial Intelligence for All, Responsibly</p><p>As we look towards the turn of another year, it is a time of introspection for many organizations. Annual forecasting, budgeting, and planning efforts are likely in progress by now, often bringing increased stress and anxiety to the C-Suite and the managers that report to them. Everyone is asked to predict the future based on entirely insufficient information. No one knows what will happen, so there are usually no alternatives to good old-fashioned guessing. Sure, you can build a spreadsheet, do elaborate calculations, and ultimately convince yourself that your forecasts are spot-on. Still, overwhelming research shows human judgement is prone to error (bias and noise). So, how do you make the best guesses you can? What tools and technologies can help you guess better?</p><p>Enter Artificial Intelligence (AI). The availability of virtually unlimited and relatively inexpensive compute and storage capabilities in the cloud today, along with advances in both the capabilities and usability of AI software, offer unprecedented opportunities to ‘crunch the numbers in new and different ways. We are now well past the point where computers are more effective than people at many data-based tasks, including searching, extrapolating, interpolating, predicting, and even winning strategy and video games. In other words, computers have gotten much better at guessing and making judgements than we are!</p><p>In the not-too-distant past, designing, building, testing, and delivering working solutions based on artificial intelligence and machine learning (AI/ML) was complicated and expensive. Fortunately, state-of-the-art. AI/ML technology has been evolving rapidly over the last few years, and several major vendors now offer powerful tools and capabilities at price points that are reasonable for nearly all organizations. In fact, according to Deloitte’s State of AI in the Enterprise survey for the 2022 edition, 94% of the 2600+ business leaders surveyed agree that AI is critical to success over the next five years. A recent Accenture survey of over 1,600 C-suite executives and data-science leaders from the world’s largest organizations found that nearly 75% of companies have integrated AI into their business strategies and reworked their cloud plans to achieve AI success. While adoption does remain somewhat higher in the enterprise space, a recent report from McKinsey showed that 56% of all respondents report AI adoption in at least one function.</p><p>Microsoft is one of the leaders in data science and management and has invested heavily for years in expanding AI/ML capabilities across many of its products and platforms. The Microsoft Azure cloud offers various services and tools that make it relatively easy to create robust AI/ML solutions for many scenarios. Microsoft has taken the integration of these technologies with core products to a new level with the recent introduction of Syntex, which brings ‘Content AI’ into SharePoint. It enables productivity-boosting functionality, including automatic document recognition, classification, summarization, translation, automated content generation, automated document assembly, AI-powered search, and active dynamic security/compliance/resilience.</p><p>In addition to AI-based content processing, there are a lot of other potentially compelling scenarios for many types of organizations, regardless of size, industry, business type, etc. Some of the most common examples of successful AI/ML outcomes include the following:</p><ul><li><strong>General decision support</strong> – Predict likely effects of various options</li><li><strong>Interpreting unstructured content</strong> – Search, identification, organization, reuse</li><li><strong>Automated document classification</strong> – Assign content types, security levels, etc.</li><li><strong>Auditing, compliance, eDiscovery</strong> – Understand what you have, where it is, how it is used</li><li><strong>Resume analysis</strong> – Interpret and rate candidates based on machine learning</li><li><strong>Anomaly detection</strong> – Identify outliers, unexpected results, and process bottlenecks</li><li><strong>Customer churn reduction</strong> – Identify at-risk customers in time to take corrective action</li><li><strong>Support process optimization</strong> – Analyze support tickets, response/resolution times, issue types</li><li><strong>Language processing</strong> – Interpret and translate between languages, natural language queries</li><li><strong>Image recognition and classification</strong> – Identify and tag pictures and graphics</li><li><strong>Audio and video transcription</strong> – Turn sounds and images into text</li><li><strong>Content generation</strong> – Create content and media based on similar existing items, text-to-speech</li></ul><p>However, all the “magic” that AI might seem to promise has a critical caveat. Along with the exciting possibilities that AI/ML offers, it is essential not to lose sight of the potential risks that can arise if care is not taken in implementing solutions. Artificial Intelligence can deliver substantial benefits to organizations that successfully leverage its power. But, if executed without attention to ethical, legal, and other people-centric and societal considerations, AI could also cause significant or even calamitous damage to a company’s reputation and prospects.</p><p>To ensure maximum value while minimizing the corresponding risks, it is helpful to have a ‘frame of reference to assess AI technologies. In that spirit, Deloitte has developed a valuable paradigm for evaluating AI-based solutions. The six dimensions in the Trustworthy AI™ framework are:</p><ul><li><strong>Fair and impartial</strong> – Help participants understand how their data can be used and how AI systems make decisions. Algorithms, attributes, and correlations are open to inspection.</li><li><strong>Transparent and explainable</strong> – Assess whether AI systems include internal and external checks to help enable equitable application across all participants.</li><li><strong>Responsible and accountable</strong> – Put an organizational structure and policies in place that can help determine who is responsible for the output of AI system decisions.</li><li><strong>Robust and reliable</strong> – Confirm that AI systems can learn from humans and other systems and produce consistent and reliable results.</li><li><strong>Respectful of privacy</strong> – Respect data privacy and avoid using AI to leverage customer data beyond its intended and stated use. Allow customers to opt in and out of sharing their data.</li><li><strong>Safe and secure</strong> – Protect AI systems from potential risks (including cyber threats) that may cause physical and digital harm.</li></ul><p>Whether your business is a Fortune 500 firm or a small organization with limited resources, there are almost certainly one or more areas where Artificial Intelligence / Machine Learning solutions could significantly impact the bottom line. Of course, given some recent survey results, you can be sure that other organizations in your space are also exploring the big, beautiful, and slightly scary world of AI/ML. Don’t be too late for the party but be careful on your way there!</p><p><strong>DISCLAIMER</strong></p><p>Copyright ©2022 by DivIHN Integration Inc.| <a href=\"mailto:kannan@divihn.com\">yoursuccess@divihn.com.</a></p><p><em>The creator of the document reserves all rights. Publication Date: November 2022. DivIHN Integration Inc. reserves the right to change the contents of this article, the features or the scope without the obligation to notify anyone of such changes. The content has been adapted using secondary research from various data points via “Google Search”. Infographics and Images used in the document are the property of the respective owners and have been used for indicative purposes only. The author reserves the right to authorize and use the Intellectual Property contained in the document.</em></p>','','full_html'),('featured_article',0,74,675,'en',0,'<p>Artificial Intelligence for All, Responsibly</p><p>As we look towards the turn of another year, it is a time of introspection for many organizations. Annual forecasting, budgeting, and planning efforts are likely in progress by now, often bringing increased stress and anxiety to the C-Suite and the managers that report to them. Everyone is asked to predict the future based on entirely insufficient information. No one knows what will happen, so there are usually no alternatives to good old-fashioned guessing. Sure, you can build a spreadsheet, do elaborate calculations, and ultimately convince yourself that your forecasts are spot-on. Still, overwhelming research shows human judgement is prone to error (bias and noise). So, how do you make the best guesses you can? What tools and technologies can help you guess better?</p><p>Enter Artificial Intelligence (AI). The availability of virtually unlimited and relatively inexpensive compute and storage capabilities in the cloud today, along with advances in both the capabilities and usability of AI software, offer unprecedented opportunities to ‘crunch the numbers in new and different ways. We are now well past the point where computers are more effective than people at many data-based tasks, including searching, extrapolating, interpolating, predicting, and even winning strategy and video games. In other words, computers have gotten much better at guessing and making judgements than we are!</p><p>In the not-too-distant past, designing, building, testing, and delivering working solutions based on artificial intelligence and machine learning (AI/ML) was complicated and expensive. Fortunately, state-of-the-art. AI/ML technology has been evolving rapidly over the last few years, and several major vendors now offer powerful tools and capabilities at price points that are reasonable for nearly all organizations. In fact, according to Deloitte’s State of AI in the Enterprise survey for the 2022 edition, 94% of the 2600+ business leaders surveyed agree that AI is critical to success over the next five years. A recent Accenture survey of over 1,600 C-suite executives and data-science leaders from the world’s largest organizations found that nearly 75% of companies have integrated AI into their business strategies and reworked their cloud plans to achieve AI success. While adoption does remain somewhat higher in the enterprise space, a recent report from McKinsey showed that 56% of all respondents report AI adoption in at least one function.</p><p>Microsoft is one of the leaders in data science and management and has invested heavily for years in expanding AI/ML capabilities across many of its products and platforms. The Microsoft Azure cloud offers various services and tools that make it relatively easy to create robust AI/ML solutions for many scenarios. Microsoft has taken the integration of these technologies with core products to a new level with the recent introduction of Syntex, which brings ‘Content AI’ into SharePoint. It enables productivity-boosting functionality, including automatic document recognition, classification, summarization, translation, automated content generation, automated document assembly, AI-powered search, and active dynamic security/compliance/resilience.</p><p>In addition to AI-based content processing, there are a lot of other potentially compelling scenarios for many types of organizations, regardless of size, industry, business type, etc. Some of the most common examples of successful AI/ML outcomes include the following:</p><ul><li><strong>General decision support</strong> – Predict likely effects of various options</li><li><strong>Interpreting unstructured content</strong> – Search, identification, organization, reuse</li><li><strong>Automated document classification</strong> – Assign content types, security levels, etc.</li><li><strong>Auditing, compliance, eDiscovery</strong> – Understand what you have, where it is, how it is used</li><li><strong>Resume analysis</strong> – Interpret and rate candidates based on machine learning</li><li><strong>Anomaly detection</strong> – Identify outliers, unexpected results, and process bottlenecks</li><li><strong>Customer churn reduction</strong> – Identify at-risk customers in time to take corrective action</li><li><strong>Support process optimization</strong> – Analyze support tickets, response/resolution times, issue types</li><li><strong>Language processing</strong> – Interpret and translate between languages, natural language queries</li><li><strong>Image recognition and classification</strong> – Identify and tag pictures and graphics</li><li><strong>Audio and video transcription</strong> – Turn sounds and images into text</li><li><strong>Content generation</strong> – Create content and media based on similar existing items, text-to-speech</li></ul><p>However, all the “magic” that AI might seem to promise has a critical caveat. Along with the exciting possibilities that AI/ML offers, it is essential not to lose sight of the potential risks that can arise if care is not taken in implementing solutions. Artificial Intelligence can deliver substantial benefits to organizations that successfully leverage its power. But, if executed without attention to ethical, legal, and other people-centric and societal considerations, AI could also cause significant or even calamitous damage to a company’s reputation and prospects.</p><p>To ensure maximum value while minimizing the corresponding risks, it is helpful to have a ‘frame of reference to assess AI technologies. In that spirit, Deloitte has developed a valuable paradigm for evaluating AI-based solutions. The six dimensions in the Trustworthy AI™ framework are:</p><ul><li><strong>Fair and impartial</strong> – Help participants understand how their data can be used and how AI systems make decisions. Algorithms, attributes, and correlations are open to inspection.</li><li><strong>Transparent and explainable</strong> – Assess whether AI systems include internal and external checks to help enable equitable application across all participants.</li><li><strong>Responsible and accountable</strong> – Put an organizational structure and policies in place that can help determine who is responsible for the output of AI system decisions.</li><li><strong>Robust and reliable</strong> – Confirm that AI systems can learn from humans and other systems and produce consistent and reliable results.</li><li><strong>Respectful of privacy</strong> – Respect data privacy and avoid using AI to leverage customer data beyond its intended and stated use. Allow customers to opt in and out of sharing their data.</li><li><strong>Safe and secure</strong> – Protect AI systems from potential risks (including cyber threats) that may cause physical and digital harm.</li></ul><p>Whether your business is a Fortune 500 firm or a small organization with limited resources, there are almost certainly one or more areas where Artificial Intelligence / Machine Learning solutions could significantly impact the bottom line. Of course, given some recent survey results, you can be sure that other organizations in your space are also exploring the big, beautiful, and slightly scary world of AI/ML. Don’t be too late for the party but be careful on your way there!</p><p><strong>DISCLAIMER</strong></p><p>Copyright ©2022 by DivIHN Integration Inc.| <a href=\"mailto:kannan@divihn.com\">yoursuccess@divihn.com.</a></p><p><em>The creator of the document reserves all rights. Publication Date: November 2022. DivIHN Integration Inc. reserves the right to change the contents of this article, the features or the scope without the obligation to notify anyone of such changes. The content has been adapted using secondary research from various data points via “Google Search”. Infographics and Images used in the document are the property of the respective owners and have been used for indicative purposes only. The author reserves the right to authorize and use the Intellectual Property contained in the document.</em></p>','','full_html'),('featured_article',0,74,853,'en',0,'<p>Artificial Intelligence for All, Responsibly</p><p>As we look towards the turn of another year, it is a time of introspection for many organizations. Annual forecasting, budgeting, and planning efforts are likely in progress by now, often bringing increased stress and anxiety to the C-Suite and the managers that report to them. Everyone is asked to predict the future based on entirely insufficient information. No one knows what will happen, so there are usually no alternatives to good old-fashioned guessing. Sure, you can build a spreadsheet, do elaborate calculations, and ultimately convince yourself that your forecasts are spot-on. Still, overwhelming research shows human judgement is prone to error (bias and noise). So, how do you make the best guesses you can? What tools and technologies can help you guess better?</p><p>Enter Artificial Intelligence (AI). The availability of virtually unlimited and relatively inexpensive compute and storage capabilities in the cloud today, along with advances in both the capabilities and usability of AI software, offer unprecedented opportunities to ‘crunch the numbers in new and different ways. We are now well past the point where computers are more effective than people at many data-based tasks, including searching, extrapolating, interpolating, predicting, and even winning strategy and video games. In other words, computers have gotten much better at guessing and making judgements than we are!</p><p>In the not-too-distant past, designing, building, testing, and delivering working solutions based on artificial intelligence and machine learning (AI/ML) was complicated and expensive. Fortunately, state-of-the-art. AI/ML technology has been evolving rapidly over the last few years, and several major vendors now offer powerful tools and capabilities at price points that are reasonable for nearly all organizations. In fact, according to Deloitte’s State of AI in the Enterprise survey for the 2022 edition, 94% of the 2600+ business leaders surveyed agree that AI is critical to success over the next five years. A recent Accenture survey of over 1,600 C-suite executives and data-science leaders from the world’s largest organizations found that nearly 75% of companies have integrated AI into their business strategies and reworked their cloud plans to achieve AI success. While adoption does remain somewhat higher in the enterprise space, a recent report from McKinsey showed that 56% of all respondents report AI adoption in at least one function.</p><p>Microsoft is one of the leaders in data science and management and has invested heavily for years in expanding AI/ML capabilities across many of its products and platforms. The Microsoft Azure cloud offers various services and tools that make it relatively easy to create robust AI/ML solutions for many scenarios. Microsoft has taken the integration of these technologies with core products to a new level with the recent introduction of Syntex, which brings ‘Content AI’ into SharePoint. It enables productivity-boosting functionality, including automatic document recognition, classification, summarization, translation, automated content generation, automated document assembly, AI-powered search, and active dynamic security/compliance/resilience.</p><p>In addition to AI-based content processing, there are a lot of other potentially compelling scenarios for many types of organizations, regardless of size, industry, business type, etc. Some of the most common examples of successful AI/ML outcomes include the following:</p><ul><li><strong>General decision support</strong> – Predict likely effects of various options</li><li><strong>Interpreting unstructured content</strong> – Search, identification, organization, reuse</li><li><strong>Automated document classification</strong> – Assign content types, security levels, etc.</li><li><strong>Auditing, compliance, eDiscovery</strong> – Understand what you have, where it is, how it is used</li><li><strong>Resume analysis</strong> – Interpret and rate candidates based on machine learning</li><li><strong>Anomaly detection</strong> – Identify outliers, unexpected results, and process bottlenecks</li><li><strong>Customer churn reduction</strong> – Identify at-risk customers in time to take corrective action</li><li><strong>Support process optimization</strong> – Analyze support tickets, response/resolution times, issue types</li><li><strong>Language processing</strong> – Interpret and translate between languages, natural language queries</li><li><strong>Image recognition and classification</strong> – Identify and tag pictures and graphics</li><li><strong>Audio and video transcription</strong> – Turn sounds and images into text</li><li><strong>Content generation</strong> – Create content and media based on similar existing items, text-to-speech</li></ul><p>However, all the “magic” that AI might seem to promise has a critical caveat. Along with the exciting possibilities that AI/ML offers, it is essential not to lose sight of the potential risks that can arise if care is not taken in implementing solutions. Artificial Intelligence can deliver substantial benefits to organizations that successfully leverage its power. But, if executed without attention to ethical, legal, and other people-centric and societal considerations, AI could also cause significant or even calamitous damage to a company’s reputation and prospects.</p><p>To ensure maximum value while minimizing the corresponding risks, it is helpful to have a ‘frame of reference to assess AI technologies. In that spirit, Deloitte has developed a valuable paradigm for evaluating AI-based solutions. The six dimensions in the Trustworthy AI™ framework are:</p><ul><li><strong>Fair and impartial</strong> – Help participants understand how their data can be used and how AI systems make decisions. Algorithms, attributes, and correlations are open to inspection.</li><li><strong>Transparent and explainable</strong> – Assess whether AI systems include internal and external checks to help enable equitable application across all participants.</li><li><strong>Responsible and accountable</strong> – Put an organizational structure and policies in place that can help determine who is responsible for the output of AI system decisions.</li><li><strong>Robust and reliable</strong> – Confirm that AI systems can learn from humans and other systems and produce consistent and reliable results.</li><li><strong>Respectful of privacy</strong> – Respect data privacy and avoid using AI to leverage customer data beyond its intended and stated use. Allow customers to opt in and out of sharing their data.</li><li><strong>Safe and secure</strong> – Protect AI systems from potential risks (including cyber threats) that may cause physical and digital harm.</li></ul><p>Whether your business is a Fortune 500 firm or a small organization with limited resources, there are almost certainly one or more areas where Artificial Intelligence / Machine Learning solutions could significantly impact the bottom line. Of course, given some recent survey results, you can be sure that other organizations in your space are also exploring the big, beautiful, and slightly scary world of AI/ML. Don’t be too late for the party but be careful on your way there!</p><p><strong>DISCLAIMER</strong></p><p>Copyright ©2022 by DivIHN Integration Inc.| <a href=\"mailto:kannan@divihn.com\">yoursuccess@divihn.com.</a></p><p><em>The creator of the document reserves all rights. Publication Date: November 2022. DivIHN Integration Inc. reserves the right to change the contents of this article, the features or the scope without the obligation to notify anyone of such changes. The content has been adapted using secondary research from various data points via “Google Search”. Infographics and Images used in the document are the property of the respective owners and have been used for indicative purposes only. The author reserves the right to authorize and use the Intellectual Property contained in the document.</em></p>','','full_html'),('featured_article',0,74,854,'en',0,'<p>Artificial Intelligence for All, Responsibly</p><p>As we look towards the turn of another year, it is a time of introspection for many organizations. Annual forecasting, budgeting, and planning efforts are likely in progress by now, often bringing increased stress and anxiety to the C-Suite and the managers that report to them. Everyone is asked to predict the future based on entirely insufficient information. No one knows what will happen, so there are usually no alternatives to good old-fashioned guessing. Sure, you can build a spreadsheet, do elaborate calculations, and ultimately convince yourself that your forecasts are spot-on. Still, overwhelming research shows human judgement is prone to error (bias and noise). So, how do you make the best guesses you can? What tools and technologies can help you guess better?</p><p>Enter Artificial Intelligence (AI). The availability of virtually unlimited and relatively inexpensive compute and storage capabilities in the cloud today, along with advances in both the capabilities and usability of AI software, offer unprecedented opportunities to ‘crunch the numbers in new and different ways. We are now well past the point where computers are more effective than people at many data-based tasks, including searching, extrapolating, interpolating, predicting, and even winning strategy and video games. In other words, computers have gotten much better at guessing and making judgements than we are!</p><p>In the not-too-distant past, designing, building, testing, and delivering working solutions based on artificial intelligence and machine learning (AI/ML) was complicated and expensive. Fortunately, state-of-the-art. AI/ML technology has been evolving rapidly over the last few years, and several major vendors now offer powerful tools and capabilities at price points that are reasonable for nearly all organizations. In fact, according to Deloitte’s State of AI in the Enterprise survey for the 2022 edition, 94% of the 2600+ business leaders surveyed agree that AI is critical to success over the next five years. A recent Accenture survey of over 1,600 C-suite executives and data-science leaders from the world’s largest organizations found that nearly 75% of companies have integrated AI into their business strategies and reworked their cloud plans to achieve AI success. While adoption does remain somewhat higher in the enterprise space, a recent report from McKinsey showed that 56% of all respondents report AI adoption in at least one function.</p><p>Microsoft is one of the leaders in data science and management and has invested heavily for years in expanding AI/ML capabilities across many of its products and platforms. The Microsoft Azure cloud offers various services and tools that make it relatively easy to create robust AI/ML solutions for many scenarios. Microsoft has taken the integration of these technologies with core products to a new level with the recent introduction of Syntex, which brings ‘Content AI’ into SharePoint. It enables productivity-boosting functionality, including automatic document recognition, classification, summarization, translation, automated content generation, automated document assembly, AI-powered search, and active dynamic security/compliance/resilience.</p><p>In addition to AI-based content processing, there are a lot of other potentially compelling scenarios for many types of organizations, regardless of size, industry, business type, etc. Some of the most common examples of successful AI/ML outcomes include the following:</p><ul><li><strong>General decision support</strong> – Predict likely effects of various options</li><li><strong>Interpreting unstructured content</strong> – Search, identification, organization, reuse</li><li><strong>Automated document classification</strong> – Assign content types, security levels, etc.</li><li><strong>Auditing, compliance, eDiscovery</strong> – Understand what you have, where it is, how it is used</li><li><strong>Resume analysis</strong> – Interpret and rate candidates based on machine learning</li><li><strong>Anomaly detection</strong> – Identify outliers, unexpected results, and process bottlenecks</li><li><strong>Customer churn reduction</strong> – Identify at-risk customers in time to take corrective action</li><li><strong>Support process optimization</strong> – Analyze support tickets, response/resolution times, issue types</li><li><strong>Language processing</strong> – Interpret and translate between languages, natural language queries</li><li><strong>Image recognition and classification</strong> – Identify and tag pictures and graphics</li><li><strong>Audio and video transcription</strong> – Turn sounds and images into text</li><li><strong>Content generation</strong> – Create content and media based on similar existing items, text-to-speech</li></ul><p>However, all the “magic” that AI might seem to promise has a critical caveat. Along with the exciting possibilities that AI/ML offers, it is essential not to lose sight of the potential risks that can arise if care is not taken in implementing solutions. Artificial Intelligence can deliver substantial benefits to organizations that successfully leverage its power. But, if executed without attention to ethical, legal, and other people-centric and societal considerations, AI could also cause significant or even calamitous damage to a company’s reputation and prospects.</p><p>To ensure maximum value while minimizing the corresponding risks, it is helpful to have a ‘frame of reference to assess AI technologies. In that spirit, Deloitte has developed a valuable paradigm for evaluating AI-based solutions. The six dimensions in the Trustworthy AI™ framework are:</p><ul><li><strong>Fair and impartial</strong> – Help participants understand how their data can be used and how AI systems make decisions. Algorithms, attributes, and correlations are open to inspection.</li><li><strong>Transparent and explainable</strong> – Assess whether AI systems include internal and external checks to help enable equitable application across all participants.</li><li><strong>Responsible and accountable</strong> – Put an organizational structure and policies in place that can help determine who is responsible for the output of AI system decisions.</li><li><strong>Robust and reliable</strong> – Confirm that AI systems can learn from humans and other systems and produce consistent and reliable results.</li><li><strong>Respectful of privacy</strong> – Respect data privacy and avoid using AI to leverage customer data beyond its intended and stated use. Allow customers to opt in and out of sharing their data.</li><li><strong>Safe and secure</strong> – Protect AI systems from potential risks (including cyber threats) that may cause physical and digital harm.</li></ul><p>Whether your business is a Fortune 500 firm or a small organization with limited resources, there are almost certainly one or more areas where Artificial Intelligence / Machine Learning solutions could significantly impact the bottom line. Of course, given some recent survey results, you can be sure that other organizations in your space are also exploring the big, beautiful, and slightly scary world of AI/ML. Don’t be too late for the party but be careful on your way there!</p><p><strong>DISCLAIMER</strong></p><p>Copyright ©2022 by DivIHN Integration Inc.| <a href=\"mailto:kannan@divihn.com\">yoursuccess@divihn.com.</a></p><p><em>The creator of the document reserves all rights. Publication Date: November 2022. DivIHN Integration Inc. reserves the right to change the contents of this article, the features or the scope without the obligation to notify anyone of such changes. The content has been adapted using secondary research from various data points via “Google Search”. Infographics and Images used in the document are the property of the respective owners and have been used for indicative purposes only. The author reserves the right to authorize and use the Intellectual Property contained in the document.</em></p>','','full_html'),('blog',0,76,200,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>In 1996 and 1997, reigning world chess champion Garry Kasparov played two six-game matches against an IBM computer called Deep Blue. Kasparov won the first match. Deep Blue was victorious in the second match, marking the first time that a computer had defeated a reigning world champion. Today, any of us could buy an inexpensive chess program for our laptop computer which would consistently outplay Deep Blue. In 1997 however, Deep Blue’s victory was a milestone in the development of artificial intelligence (AI). There was much speculation at the time about the future of AI and about the similarities of computing machines and the human mind. Yale University professor David Gelernter urged restraint.</p><p>“The idea that Deep Blue has a mind is absurd. How can an object that wants nothing, fears nothing, enjoys nothing, needs nothing, and cares about nothing have a mind? It can win at chess, but not because it wants to. It isn’t happy when it wins or sad when it loses. What are its [post]-match plans if it beats Kasparov? Is it hoping to take Deep Pink out for a night on the town?” (Gelernter, 1997)</p><p>Ask ten people to define “artificial intelligence” and you will get ten different answers. This is, in some ways, beneficial ambiguity. Firm definitions can be needlessly constraining to those whose role it is to innovate. According to Stone et al at Stanford University, “the lack of a precise, universally accepted definition of AI probably has helped the field to grow, blossom, and advance at an ever-accelerating pace.” (Stanford, 2016). Nevertheless, to talk about a thing intelligently, it is worth a paragraph or two to define it as it is used in the present discussion. The word “artificial” is relatively easy to define. We use it to refer to things that do not occur naturally, things that are created by humans. The word “intelligence” is a bit more difficult. Philosophers, psychologists, anthropologists, and even theologians offer a variety of definitions. For the purposes of this article series, we will start with a definition which focuses on problem-solving. Psychologist Howard Gardner wrote, “Intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings” (Gardner, 1983).</p><p>Thus, artificial intelligence or AI, as we shall use it here, is a machine capability, which does not occur naturally, to solve problems or create something of value. We call this the realm of narrow AI, where an intelligent apparatus is created to solve a particular kind of problem or create a particular type of valuable thing. General AI, which theoretically includes non-trivial foresight, independent problem identification, and problem prioritization based on something like a human value system, or human-like hopes and dreams does not yet exist. In Gelernter’s view, “the gap between the human and the [electronic] surrogate is permanent… Machines will continue to make life easier, healthier, richer, and more puzzling. And humans will continue to care, ultimately, about the same things they always have: about themselves, about one another, and many of them, about God.” (Gelernter, 1976)</p><p>In the next installment, we will consider, “What does AI offer humankind?”</p><p>Notes and references:</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstream Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020/</a></p><p>Gardner H., 1983, Frames of Mind: The Theory of Multiple Intelligences. New York, Fontana Press.</p><p>Gelernter, D., 1997, How Hard Is Chess? TIME, (May 19, 1997)</p><p>Stone P., Brooks R., Brynjolfsson E., Calo R., Etzioni O., Hager G., Hirschberg J., Kalyanakrishnan S., Kamar E., Kraus S., Leyton-Brown K., Parkes D., Press W., Saxenian A., Shah J., Tambe M., and Teller A., 2016. Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016.<a href=\"http://ai100.stanford.edu/2016-report\">http://ai100.stanford.edu/2016-report</a></p><p><strong>Posts in this series</strong></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,76,243,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>In 1996 and 1997, reigning world chess champion Garry Kasparov played two six-game matches against an IBM computer called Deep Blue. Kasparov won the first match. Deep Blue was victorious in the second match, marking the first time that a computer had defeated a reigning world champion. Today, any of us could buy an inexpensive chess program for our laptop computer which would consistently outplay Deep Blue. In 1997 however, Deep Blue’s victory was a milestone in the development of artificial intelligence (AI). There was much speculation at the time about the future of AI and about the similarities of computing machines and the human mind. Yale University professor David Gelernter urged restraint.</p><p>“The idea that Deep Blue has a mind is absurd. How can an object that wants nothing, fears nothing, enjoys nothing, needs nothing, and cares about nothing have a mind? It can win at chess, but not because it wants to. It isn’t happy when it wins or sad when it loses. What are its [post]-match plans if it beats Kasparov? Is it hoping to take Deep Pink out for a night on the town?” (Gelernter, 1997)</p><p>Ask ten people to define “artificial intelligence” and you will get ten different answers. This is, in some ways, beneficial ambiguity. Firm definitions can be needlessly constraining to those whose role it is to innovate. According to Stone et al at Stanford University, “the lack of a precise, universally accepted definition of AI probably has helped the field to grow, blossom, and advance at an ever-accelerating pace.” (Stanford, 2016). Nevertheless, to talk about a thing intelligently, it is worth a paragraph or two to define it as it is used in the present discussion. The word “artificial” is relatively easy to define. We use it to refer to things that do not occur naturally, things that are created by humans. The word “intelligence” is a bit more difficult. Philosophers, psychologists, anthropologists, and even theologians offer a variety of definitions. For the purposes of this article series, we will start with a definition which focuses on problem-solving. Psychologist Howard Gardner wrote, “Intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings” (Gardner, 1983).</p><p>Thus, artificial intelligence or AI, as we shall use it here, is a machine capability, which does not occur naturally, to solve problems or create something of value. We call this the realm of narrow AI, where an intelligent apparatus is created to solve a particular kind of problem or create a particular type of valuable thing. General AI, which theoretically includes non-trivial foresight, independent problem identification, and problem prioritization based on something like a human value system, or human-like hopes and dreams does not yet exist. In Gelernter’s view, “the gap between the human and the [electronic] surrogate is permanent… Machines will continue to make life easier, healthier, richer, and more puzzling. And humans will continue to care, ultimately, about the same things they always have: about themselves, about one another, and many of them, about God.” (Gelernter, 1976)</p><p>In the next installment, we will consider, “What does AI offer humankind?”</p><p>Notes and references:</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstream Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020/</a></p><p>Gardner H., 1983, Frames of Mind: The Theory of Multiple Intelligences. New York, Fontana Press.</p><p>Gelernter, D., 1997, How Hard Is Chess? TIME, (May 19, 1997)</p><p>Stone P., Brooks R., Brynjolfsson E., Calo R., Etzioni O., Hager G., Hirschberg J., Kalyanakrishnan S., Kamar E., Kraus S., Leyton-Brown K., Parkes D., Press W., Saxenian A., Shah J., Tambe M., and Teller A., 2016. Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016.<a href=\"http://ai100.stanford.edu/2016-report\">http://ai100.stanford.edu/2016-report</a></p><p><strong>Posts in this series</strong></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,76,476,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>In 1996 and 1997, reigning world chess champion Garry Kasparov played two six-game matches against an IBM computer called Deep Blue. Kasparov won the first match. Deep Blue was victorious in the second match, marking the first time that a computer had defeated a reigning world champion. Today, any of us could buy an inexpensive chess program for our laptop computer which would consistently outplay Deep Blue. In 1997 however, Deep Blue’s victory was a milestone in the development of artificial intelligence (AI). There was much speculation at the time about the future of AI and about the similarities of computing machines and the human mind. Yale University professor David Gelernter urged restraint.</p><p>“The idea that Deep Blue has a mind is absurd. How can an object that wants nothing, fears nothing, enjoys nothing, needs nothing, and cares about nothing have a mind? It can win at chess, but not because it wants to. It isn’t happy when it wins or sad when it loses. What are its [post]-match plans if it beats Kasparov? Is it hoping to take Deep Pink out for a night on the town?” (Gelernter, 1997)</p><p>Ask ten people to define “artificial intelligence” and you will get ten different answers. This is, in some ways, beneficial ambiguity. Firm definitions can be needlessly constraining to those whose role it is to innovate. According to Stone et al at Stanford University, “the lack of a precise, universally accepted definition of AI probably has helped the field to grow, blossom, and advance at an ever-accelerating pace.” (Stanford, 2016). Nevertheless, to talk about a thing intelligently, it is worth a paragraph or two to define it as it is used in the present discussion. The word “artificial” is relatively easy to define. We use it to refer to things that do not occur naturally, things that are created by humans. The word “intelligence” is a bit more difficult. Philosophers, psychologists, anthropologists, and even theologians offer a variety of definitions. For the purposes of this article series, we will start with a definition which focuses on problem-solving. Psychologist Howard Gardner wrote, “Intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings” (Gardner, 1983).</p><p>Thus, artificial intelligence or AI, as we shall use it here, is a machine capability, which does not occur naturally, to solve problems or create something of value. We call this the realm of narrow AI, where an intelligent apparatus is created to solve a particular kind of problem or create a particular type of valuable thing. General AI, which theoretically includes non-trivial foresight, independent problem identification, and problem prioritization based on something like a human value system, or human-like hopes and dreams does not yet exist. In Gelernter’s view, “the gap between the human and the [electronic] surrogate is permanent… Machines will continue to make life easier, healthier, richer, and more puzzling. And humans will continue to care, ultimately, about the same things they always have: about themselves, about one another, and many of them, about God.” (Gelernter, 1976)</p><p>In the next installment, we will consider, “What does AI offer humankind?”</p><p>Notes and references:</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstream Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020/</a></p><p>Gardner H., 1983, Frames of Mind: The Theory of Multiple Intelligences. New York, Fontana Press.</p><p>Gelernter, D., 1997, How Hard Is Chess? TIME, (May 19, 1997)</p><p>Stone P., Brooks R., Brynjolfsson E., Calo R., Etzioni O., Hager G., Hirschberg J., Kalyanakrishnan S., Kamar E., Kraus S., Leyton-Brown K., Parkes D., Press W., Saxenian A., Shah J., Tambe M., and Teller A., 2016. Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016.<a href=\"http://ai100.stanford.edu/2016-report\">http://ai100.stanford.edu/2016-report</a></p><p><strong>Posts in this series</strong></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,76,556,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>In 1996 and 1997, reigning world chess champion Garry Kasparov played two six-game matches against an IBM computer called Deep Blue. Kasparov won the first match. Deep Blue was victorious in the second match, marking the first time that a computer had defeated a reigning world champion. Today, any of us could buy an inexpensive chess program for our laptop computer which would consistently outplay Deep Blue. In 1997 however, Deep Blue’s victory was a milestone in the development of artificial intelligence (AI). There was much speculation at the time about the future of AI and about the similarities of computing machines and the human mind. Yale University professor David Gelernter urged restraint.</p><p>“The idea that Deep Blue has a mind is absurd. How can an object that wants nothing, fears nothing, enjoys nothing, needs nothing, and cares about nothing have a mind? It can win at chess, but not because it wants to. It isn’t happy when it wins or sad when it loses. What are its [post]-match plans if it beats Kasparov? Is it hoping to take Deep Pink out for a night on the town?” (Gelernter, 1997)</p><p>Ask ten people to define “artificial intelligence” and you will get ten different answers. This is, in some ways, beneficial ambiguity. Firm definitions can be needlessly constraining to those whose role it is to innovate. According to Stone et al at Stanford University, “the lack of a precise, universally accepted definition of AI probably has helped the field to grow, blossom, and advance at an ever-accelerating pace.” (Stanford, 2016). Nevertheless, to talk about a thing intelligently, it is worth a paragraph or two to define it as it is used in the present discussion. The word “artificial” is relatively easy to define. We use it to refer to things that do not occur naturally, things that are created by humans. The word “intelligence” is a bit more difficult. Philosophers, psychologists, anthropologists, and even theologians offer a variety of definitions. For the purposes of this article series, we will start with a definition which focuses on problem-solving. Psychologist Howard Gardner wrote, “Intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings” (Gardner, 1983).</p><p>Thus, artificial intelligence or AI, as we shall use it here, is a machine capability, which does not occur naturally, to solve problems or create something of value. We call this the realm of narrow AI, where an intelligent apparatus is created to solve a particular kind of problem or create a particular type of valuable thing. General AI, which theoretically includes non-trivial foresight, independent problem identification, and problem prioritization based on something like a human value system, or human-like hopes and dreams does not yet exist. In Gelernter’s view, “the gap between the human and the [electronic] surrogate is permanent… Machines will continue to make life easier, healthier, richer, and more puzzling. And humans will continue to care, ultimately, about the same things they always have: about themselves, about one another, and many of them, about God.” (Gelernter, 1976)</p><p>In the next installment, we will consider, “What does AI offer humankind?”</p><p>Notes and references:</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstream Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020/</a></p><p>Gardner H., 1983, Frames of Mind: The Theory of Multiple Intelligences. New York, Fontana Press.</p><p>Gelernter, D., 1997, How Hard Is Chess? TIME, (May 19, 1997)</p><p>Stone P., Brooks R., Brynjolfsson E., Calo R., Etzioni O., Hager G., Hirschberg J., Kalyanakrishnan S., Kamar E., Kraus S., Leyton-Brown K., Parkes D., Press W., Saxenian A., Shah J., Tambe M., and Teller A., 2016. Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016.<a href=\"http://ai100.stanford.edu/2016-report\">http://ai100.stanford.edu/2016-report</a></p><p><strong>Posts in this series</strong></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,76,631,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>In 1996 and 1997, reigning world chess champion Garry Kasparov played two six-game matches against an IBM computer called Deep Blue. Kasparov won the first match. Deep Blue was victorious in the second match, marking the first time that a computer had defeated a reigning world champion. Today, any of us could buy an inexpensive chess program for our laptop computer which would consistently outplay Deep Blue. In 1997 however, Deep Blue’s victory was a milestone in the development of artificial intelligence (AI). There was much speculation at the time about the future of AI and about the similarities of computing machines and the human mind. Yale University professor David Gelernter urged restraint.</p><p>“The idea that Deep Blue has a mind is absurd. How can an object that wants nothing, fears nothing, enjoys nothing, needs nothing, and cares about nothing have a mind? It can win at chess, but not because it wants to. It isn’t happy when it wins or sad when it loses. What are its [post]-match plans if it beats Kasparov? Is it hoping to take Deep Pink out for a night on the town?” (Gelernter, 1997)</p><p>Ask ten people to define “artificial intelligence” and you will get ten different answers. This is, in some ways, beneficial ambiguity. Firm definitions can be needlessly constraining to those whose role it is to innovate. According to Stone et al at Stanford University, “the lack of a precise, universally accepted definition of AI probably has helped the field to grow, blossom, and advance at an ever-accelerating pace.” (Stanford, 2016). Nevertheless, to talk about a thing intelligently, it is worth a paragraph or two to define it as it is used in the present discussion. The word “artificial” is relatively easy to define. We use it to refer to things that do not occur naturally, things that are created by humans. The word “intelligence” is a bit more difficult. Philosophers, psychologists, anthropologists, and even theologians offer a variety of definitions. For the purposes of this article series, we will start with a definition which focuses on problem-solving. Psychologist Howard Gardner wrote, “Intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings” (Gardner, 1983).</p><p>Thus, artificial intelligence or AI, as we shall use it here, is a machine capability, which does not occur naturally, to solve problems or create something of value. We call this the realm of narrow AI, where an intelligent apparatus is created to solve a particular kind of problem or create a particular type of valuable thing. General AI, which theoretically includes non-trivial foresight, independent problem identification, and problem prioritization based on something like a human value system, or human-like hopes and dreams does not yet exist. In Gelernter’s view, “the gap between the human and the [electronic] surrogate is permanent… Machines will continue to make life easier, healthier, richer, and more puzzling. And humans will continue to care, ultimately, about the same things they always have: about themselves, about one another, and many of them, about God.” (Gelernter, 1976)</p><p>In the next installment, we will consider, “What does AI offer humankind?”</p><p>Notes and references:</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstream Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020/</a></p><p>Gardner H., 1983, Frames of Mind: The Theory of Multiple Intelligences. New York, Fontana Press.</p><p>Gelernter, D., 1997, How Hard Is Chess? TIME, (May 19, 1997)</p><p>Stone P., Brooks R., Brynjolfsson E., Calo R., Etzioni O., Hager G., Hirschberg J., Kalyanakrishnan S., Kamar E., Kraus S., Leyton-Brown K., Parkes D., Press W., Saxenian A., Shah J., Tambe M., and Teller A., 2016. Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016.<a href=\"http://ai100.stanford.edu/2016-report\">http://ai100.stanford.edu/2016-report</a></p><p><strong>Posts in this series</strong></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,76,806,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>In 1996 and 1997, reigning world chess champion Garry Kasparov played two six-game matches against an IBM computer called Deep Blue. Kasparov won the first match. Deep Blue was victorious in the second match, marking the first time that a computer had defeated a reigning world champion. Today, any of us could buy an inexpensive chess program for our laptop computer which would consistently outplay Deep Blue. In 1997 however, Deep Blue’s victory was a milestone in the development of artificial intelligence (AI). There was much speculation at the time about the future of AI and about the similarities of computing machines and the human mind. Yale University professor David Gelernter urged restraint.</p><p>“The idea that Deep Blue has a mind is absurd. How can an object that wants nothing, fears nothing, enjoys nothing, needs nothing, and cares about nothing have a mind? It can win at chess, but not because it wants to. It isn’t happy when it wins or sad when it loses. What are its [post]-match plans if it beats Kasparov? Is it hoping to take Deep Pink out for a night on the town?” (Gelernter, 1997)</p><p>Ask ten people to define “artificial intelligence” and you will get ten different answers. This is, in some ways, beneficial ambiguity. Firm definitions can be needlessly constraining to those whose role it is to innovate. According to Stone et al at Stanford University, “the lack of a precise, universally accepted definition of AI probably has helped the field to grow, blossom, and advance at an ever-accelerating pace.” (Stanford, 2016). Nevertheless, to talk about a thing intelligently, it is worth a paragraph or two to define it as it is used in the present discussion. The word “artificial” is relatively easy to define. We use it to refer to things that do not occur naturally, things that are created by humans. The word “intelligence” is a bit more difficult. Philosophers, psychologists, anthropologists, and even theologians offer a variety of definitions. For the purposes of this article series, we will start with a definition which focuses on problem-solving. Psychologist Howard Gardner wrote, “Intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings” (Gardner, 1983).</p><p>Thus, artificial intelligence or AI, as we shall use it here, is a machine capability, which does not occur naturally, to solve problems or create something of value. We call this the realm of narrow AI, where an intelligent apparatus is created to solve a particular kind of problem or create a particular type of valuable thing. General AI, which theoretically includes non-trivial foresight, independent problem identification, and problem prioritization based on something like a human value system, or human-like hopes and dreams does not yet exist. In Gelernter’s view, “the gap between the human and the [electronic] surrogate is permanent… Machines will continue to make life easier, healthier, richer, and more puzzling. And humans will continue to care, ultimately, about the same things they always have: about themselves, about one another, and many of them, about God.” (Gelernter, 1976)</p><p>In the next installment, we will consider, “What does AI offer humankind?”</p><p>Notes and references:</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstream Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020/</a></p><p>Gardner H., 1983, Frames of Mind: The Theory of Multiple Intelligences. New York, Fontana Press.</p><p>Gelernter, D., 1997, How Hard Is Chess? TIME, (May 19, 1997)</p><p>Stone P., Brooks R., Brynjolfsson E., Calo R., Etzioni O., Hager G., Hirschberg J., Kalyanakrishnan S., Kamar E., Kraus S., Leyton-Brown K., Parkes D., Press W., Saxenian A., Shah J., Tambe M., and Teller A., 2016. Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016.<a href=\"http://ai100.stanford.edu/2016-report\">http://ai100.stanford.edu/2016-report</a></p><p><strong>Posts in this series</strong></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,77,201,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>Information technology in general and AI in particular serve as assistants to humankind in problem-solving and value creation. Humans can articulate problems and propose solutions beyond their capacity to effectively execute the solution. That is, some solutions require computational capacity that exceeds human capacity in scale, complexity, or both.</p><p>It is important to distinguish between scale and complexity. Humans are more than capable of adding a column of numbers. Machines are better at scale, making short work of calculating totals and averages of thousands or millions of values. Humans are still better than AI at certain kinds of complex tasks when those tasks are performed on a small scale.</p><p>Amazon’s Alexa, Apple’s Siri, and Google’s Assistant use a special kind of AI, Natural Language Processing (NLP), to recognize spoken commands and questions, and to provide synthetic replies. As complex as these tasks might be, they are not as complex as human conversation which considers not only words and grammar, but tone, and non-verbal and paraverbal cues. Human conversations take place in multiple simultaneous contexts. Words and grammar mean different things in different conversations. Thus far, NLP allows machines to process language. It does not yet enable them to converse as humans do, bringing feelings, perceptions, and imagination into the exchange of information. On the other hand, humans can converse with only a limited number of other humans at once at a limited pace. On the other hand, a machine’s ability to tirelessly process long streams of natural language, even more than one at a time, is limited only by the technical architecture of the hardware and software on which it is realized.</p><p>Figure 1 positions some illustrative problem on axes of scale and complexity. The promise of AI is in the high-complexity, low-scale and high-complexity, high-scale quadrants.</p><p><img src=\"/sites/default/files/inline-images/scale-and-complexity-1.jpg\" data-entity-uuid=\"811a7876-8da6-4989-8474-f27eaee5dfc1\" data-entity-type=\"file\" width=\"500\" height=\"445\"></p><p>This is what makes widespread applications of AI possible – exponential increases in ubiquitous computing capacity. Computers which filled entire rooms in the 1960s were capable of little more than arithmetic at scale. Today, billions of mobile phones contain the computing capacity required to perform the complex tasks which we refer to as Artificial Intelligence. Except where human “wiring” remains the better tool for some kinds of problem-solving, machines can outperform humans in both scale and complexity. This may seem obvious, but it is exactly the point of all technology. The first sharpened stone was a better cutting instrument than human fingernails. This is what we humans do. We are toolmakers. We create things that allow us to solve problems and create value better, faster, and more reliably than we can with our innate capacities. AI is just a new tool. It may be an exceptionally interesting tool because, more than a sharpened stone, we see a bit of ourselves reflected in it.</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstrem Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020</a></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,77,202,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>Information technology in general and AI in particular serve as assistants to humankind in problem-solving and value creation. Humans can articulate problems and propose solutions beyond their capacity to effectively execute the solution. That is, some solutions require computational capacity that exceeds human capacity in scale, complexity, or both.</p><p>It is important to distinguish between scale and complexity. Humans are more than capable of adding a column of numbers. Machines are better at scale, making short work of calculating totals and averages of thousands or millions of values. Humans are still better than AI at certain kinds of complex tasks when those tasks are performed on a small scale.</p><p>Amazon’s Alexa, Apple’s Siri, and Google’s Assistant use a special kind of AI, Natural Language Processing (NLP), to recognize spoken commands and questions, and to provide synthetic replies. As complex as these tasks might be, they are not as complex as human conversation which considers not only words and grammar, but tone, and non-verbal and paraverbal cues. Human conversations take place in multiple simultaneous contexts. Words and grammar mean different things in different conversations. Thus far, NLP allows machines to process language. It does not yet enable them to converse as humans do, bringing feelings, perceptions, and imagination into the exchange of information. On the other hand, humans can converse with only a limited number of other humans at once at a limited pace. On the other hand, a machine’s ability to tirelessly process long streams of natural language, even more than one at a time, is limited only by the technical architecture of the hardware and software on which it is realized.</p><p>Figure 1 positions some illustrative problem on axes of scale and complexity. The promise of AI is in the high-complexity, low-scale and high-complexity, high-scale quadrants.</p><p><img src=\"/sites/default/files/inline-images/scale-and-complexity-1.jpg\" data-entity-uuid=\"811a7876-8da6-4989-8474-f27eaee5dfc1\" data-entity-type=\"file\" width=\"500\" height=\"445\"></p><p>This is what makes widespread applications of AI possible – exponential increases in ubiquitous computing capacity. Computers which filled entire rooms in the 1960s were capable of little more than arithmetic at scale. Today, billions of mobile phones contain the computing capacity required to perform the complex tasks which we refer to as Artificial Intelligence. Except where human “wiring” remains the better tool for some kinds of problem-solving, machines can outperform humans in both scale and complexity. This may seem obvious, but it is exactly the point of all technology. The first sharpened stone was a better cutting instrument than human fingernails. This is what we humans do. We are toolmakers. We create things that allow us to solve problems and create value better, faster, and more reliably than we can with our innate capacities. AI is just a new tool. It may be an exceptionally interesting tool because, more than a sharpened stone, we see a bit of ourselves reflected in it.</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstrem Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020</a></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,77,242,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>Information technology in general and AI in particular serve as assistants to humankind in problem-solving and value creation. Humans can articulate problems and propose solutions beyond their capacity to effectively execute the solution. That is, some solutions require computational capacity that exceeds human capacity in scale, complexity, or both.</p><p>It is important to distinguish between scale and complexity. Humans are more than capable of adding a column of numbers. Machines are better at scale, making short work of calculating totals and averages of thousands or millions of values. Humans are still better than AI at certain kinds of complex tasks when those tasks are performed on a small scale.</p><p>Amazon’s Alexa, Apple’s Siri, and Google’s Assistant use a special kind of AI, Natural Language Processing (NLP), to recognize spoken commands and questions, and to provide synthetic replies. As complex as these tasks might be, they are not as complex as human conversation which considers not only words and grammar, but tone, and non-verbal and paraverbal cues. Human conversations take place in multiple simultaneous contexts. Words and grammar mean different things in different conversations. Thus far, NLP allows machines to process language. It does not yet enable them to converse as humans do, bringing feelings, perceptions, and imagination into the exchange of information. On the other hand, humans can converse with only a limited number of other humans at once at a limited pace. On the other hand, a machine’s ability to tirelessly process long streams of natural language, even more than one at a time, is limited only by the technical architecture of the hardware and software on which it is realized.</p><p>Figure 1 positions some illustrative problem on axes of scale and complexity. The promise of AI is in the high-complexity, low-scale and high-complexity, high-scale quadrants.</p><p><img src=\"/sites/default/files/inline-images/scale-and-complexity-1.jpg\" data-entity-uuid=\"811a7876-8da6-4989-8474-f27eaee5dfc1\" data-entity-type=\"file\" width=\"500\" height=\"445\"></p><p>This is what makes widespread applications of AI possible – exponential increases in ubiquitous computing capacity. Computers which filled entire rooms in the 1960s were capable of little more than arithmetic at scale. Today, billions of mobile phones contain the computing capacity required to perform the complex tasks which we refer to as Artificial Intelligence. Except where human “wiring” remains the better tool for some kinds of problem-solving, machines can outperform humans in both scale and complexity. This may seem obvious, but it is exactly the point of all technology. The first sharpened stone was a better cutting instrument than human fingernails. This is what we humans do. We are toolmakers. We create things that allow us to solve problems and create value better, faster, and more reliably than we can with our innate capacities. AI is just a new tool. It may be an exceptionally interesting tool because, more than a sharpened stone, we see a bit of ourselves reflected in it.</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstrem Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020</a></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,77,477,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>Information technology in general and AI in particular serve as assistants to humankind in problem-solving and value creation. Humans can articulate problems and propose solutions beyond their capacity to effectively execute the solution. That is, some solutions require computational capacity that exceeds human capacity in scale, complexity, or both.</p><p>It is important to distinguish between scale and complexity. Humans are more than capable of adding a column of numbers. Machines are better at scale, making short work of calculating totals and averages of thousands or millions of values. Humans are still better than AI at certain kinds of complex tasks when those tasks are performed on a small scale.</p><p>Amazon’s Alexa, Apple’s Siri, and Google’s Assistant use a special kind of AI, Natural Language Processing (NLP), to recognize spoken commands and questions, and to provide synthetic replies. As complex as these tasks might be, they are not as complex as human conversation which considers not only words and grammar, but tone, and non-verbal and paraverbal cues. Human conversations take place in multiple simultaneous contexts. Words and grammar mean different things in different conversations. Thus far, NLP allows machines to process language. It does not yet enable them to converse as humans do, bringing feelings, perceptions, and imagination into the exchange of information. On the other hand, humans can converse with only a limited number of other humans at once at a limited pace. On the other hand, a machine’s ability to tirelessly process long streams of natural language, even more than one at a time, is limited only by the technical architecture of the hardware and software on which it is realized.</p><p>Figure 1 positions some illustrative problem on axes of scale and complexity. The promise of AI is in the high-complexity, low-scale and high-complexity, high-scale quadrants.</p><p><img src=\"/sites/default/files/inline-images/scale-and-complexity-1.jpg\" data-entity-uuid=\"811a7876-8da6-4989-8474-f27eaee5dfc1\" data-entity-type=\"file\" width=\"500\" height=\"445\"></p><p>This is what makes widespread applications of AI possible – exponential increases in ubiquitous computing capacity. Computers which filled entire rooms in the 1960s were capable of little more than arithmetic at scale. Today, billions of mobile phones contain the computing capacity required to perform the complex tasks which we refer to as Artificial Intelligence. Except where human “wiring” remains the better tool for some kinds of problem-solving, machines can outperform humans in both scale and complexity. This may seem obvious, but it is exactly the point of all technology. The first sharpened stone was a better cutting instrument than human fingernails. This is what we humans do. We are toolmakers. We create things that allow us to solve problems and create value better, faster, and more reliably than we can with our innate capacities. AI is just a new tool. It may be an exceptionally interesting tool because, more than a sharpened stone, we see a bit of ourselves reflected in it.</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstrem Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020</a></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,77,557,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>Information technology in general and AI in particular serve as assistants to humankind in problem-solving and value creation. Humans can articulate problems and propose solutions beyond their capacity to effectively execute the solution. That is, some solutions require computational capacity that exceeds human capacity in scale, complexity, or both.</p><p>It is important to distinguish between scale and complexity. Humans are more than capable of adding a column of numbers. Machines are better at scale, making short work of calculating totals and averages of thousands or millions of values. Humans are still better than AI at certain kinds of complex tasks when those tasks are performed on a small scale.</p><p>Amazon’s Alexa, Apple’s Siri, and Google’s Assistant use a special kind of AI, Natural Language Processing (NLP), to recognize spoken commands and questions, and to provide synthetic replies. As complex as these tasks might be, they are not as complex as human conversation which considers not only words and grammar, but tone, and non-verbal and paraverbal cues. Human conversations take place in multiple simultaneous contexts. Words and grammar mean different things in different conversations. Thus far, NLP allows machines to process language. It does not yet enable them to converse as humans do, bringing feelings, perceptions, and imagination into the exchange of information. On the other hand, humans can converse with only a limited number of other humans at once at a limited pace. On the other hand, a machine’s ability to tirelessly process long streams of natural language, even more than one at a time, is limited only by the technical architecture of the hardware and software on which it is realized.</p><p>Figure 1 positions some illustrative problem on axes of scale and complexity. The promise of AI is in the high-complexity, low-scale and high-complexity, high-scale quadrants.</p><p><img src=\"/sites/default/files/inline-images/scale-and-complexity-1.jpg\" data-entity-uuid=\"811a7876-8da6-4989-8474-f27eaee5dfc1\" data-entity-type=\"file\" width=\"500\" height=\"445\"></p><p>This is what makes widespread applications of AI possible – exponential increases in ubiquitous computing capacity. Computers which filled entire rooms in the 1960s were capable of little more than arithmetic at scale. Today, billions of mobile phones contain the computing capacity required to perform the complex tasks which we refer to as Artificial Intelligence. Except where human “wiring” remains the better tool for some kinds of problem-solving, machines can outperform humans in both scale and complexity. This may seem obvious, but it is exactly the point of all technology. The first sharpened stone was a better cutting instrument than human fingernails. This is what we humans do. We are toolmakers. We create things that allow us to solve problems and create value better, faster, and more reliably than we can with our innate capacities. AI is just a new tool. It may be an exceptionally interesting tool because, more than a sharpened stone, we see a bit of ourselves reflected in it.</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstrem Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020</a></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,77,632,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>Information technology in general and AI in particular serve as assistants to humankind in problem-solving and value creation. Humans can articulate problems and propose solutions beyond their capacity to effectively execute the solution. That is, some solutions require computational capacity that exceeds human capacity in scale, complexity, or both.</p><p>It is important to distinguish between scale and complexity. Humans are more than capable of adding a column of numbers. Machines are better at scale, making short work of calculating totals and averages of thousands or millions of values. Humans are still better than AI at certain kinds of complex tasks when those tasks are performed on a small scale.</p><p>Amazon’s Alexa, Apple’s Siri, and Google’s Assistant use a special kind of AI, Natural Language Processing (NLP), to recognize spoken commands and questions, and to provide synthetic replies. As complex as these tasks might be, they are not as complex as human conversation which considers not only words and grammar, but tone, and non-verbal and paraverbal cues. Human conversations take place in multiple simultaneous contexts. Words and grammar mean different things in different conversations. Thus far, NLP allows machines to process language. It does not yet enable them to converse as humans do, bringing feelings, perceptions, and imagination into the exchange of information. On the other hand, humans can converse with only a limited number of other humans at once at a limited pace. On the other hand, a machine’s ability to tirelessly process long streams of natural language, even more than one at a time, is limited only by the technical architecture of the hardware and software on which it is realized.</p><p>Figure 1 positions some illustrative problem on axes of scale and complexity. The promise of AI is in the high-complexity, low-scale and high-complexity, high-scale quadrants.</p><p><img src=\"/sites/default/files/inline-images/scale-and-complexity-1.jpg\" data-entity-uuid=\"811a7876-8da6-4989-8474-f27eaee5dfc1\" data-entity-type=\"file\" width=\"500\" height=\"445\"></p><p>This is what makes widespread applications of AI possible – exponential increases in ubiquitous computing capacity. Computers which filled entire rooms in the 1960s were capable of little more than arithmetic at scale. Today, billions of mobile phones contain the computing capacity required to perform the complex tasks which we refer to as Artificial Intelligence. Except where human “wiring” remains the better tool for some kinds of problem-solving, machines can outperform humans in both scale and complexity. This may seem obvious, but it is exactly the point of all technology. The first sharpened stone was a better cutting instrument than human fingernails. This is what we humans do. We are toolmakers. We create things that allow us to solve problems and create value better, faster, and more reliably than we can with our innate capacities. AI is just a new tool. It may be an exceptionally interesting tool because, more than a sharpened stone, we see a bit of ourselves reflected in it.</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstrem Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020</a></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,77,758,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>Information technology in general and AI in particular serve as assistants to humankind in problem-solving and value creation. Humans can articulate problems and propose solutions beyond their capacity to effectively execute the solution. That is, some solutions require computational capacity that exceeds human capacity in scale, complexity, or both.</p><p>It is important to distinguish between scale and complexity. Humans are more than capable of adding a column of numbers. Machines are better at scale, making short work of calculating totals and averages of thousands or millions of values. Humans are still better than AI at certain kinds of complex tasks when those tasks are performed on a small scale.</p><p>Amazon’s Alexa, Apple’s Siri, and Google’s Assistant use a special kind of AI, Natural Language Processing (NLP), to recognize spoken commands and questions, and to provide synthetic replies. As complex as these tasks might be, they are not as complex as human conversation which considers not only words and grammar, but tone, and non-verbal and paraverbal cues. Human conversations take place in multiple simultaneous contexts. Words and grammar mean different things in different conversations. Thus far, NLP allows machines to process language. It does not yet enable them to converse as humans do, bringing feelings, perceptions, and imagination into the exchange of information. On the other hand, humans can converse with only a limited number of other humans at once at a limited pace. On the other hand, a machine’s ability to tirelessly process long streams of natural language, even more than one at a time, is limited only by the technical architecture of the hardware and software on which it is realized.</p><p>Figure 1 positions some illustrative problem on axes of scale and complexity. The promise of AI is in the high-complexity, low-scale and high-complexity, high-scale quadrants.</p><p><img src=\"/sites/default/files/inline-images/scale-and-complexity-1.jpg\" data-entity-uuid=\"811a7876-8da6-4989-8474-f27eaee5dfc1\" data-entity-type=\"file\" width=\"500\" height=\"445\" alt=\" promise of AI\"></p><p>This is what makes widespread applications of AI possible – exponential increases in ubiquitous computing capacity. Computers which filled entire rooms in the 1960s were capable of little more than arithmetic at scale. Today, billions of mobile phones contain the computing capacity required to perform the complex tasks which we refer to as Artificial Intelligence. Except where human “wiring” remains the better tool for some kinds of problem-solving, machines can outperform humans in both scale and complexity. This may seem obvious, but it is exactly the point of all technology. The first sharpened stone was a better cutting instrument than human fingernails. This is what we humans do. We are toolmakers. We create things that allow us to solve problems and create value better, faster, and more reliably than we can with our innate capacities. AI is just a new tool. It may be an exceptionally interesting tool because, more than a sharpened stone, we see a bit of ourselves reflected in it.</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstrem Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020</a></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,77,807,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>Information technology in general and AI in particular serve as assistants to humankind in problem-solving and value creation. Humans can articulate problems and propose solutions beyond their capacity to effectively execute the solution. That is, some solutions require computational capacity that exceeds human capacity in scale, complexity, or both.</p><p>It is important to distinguish between scale and complexity. Humans are more than capable of adding a column of numbers. Machines are better at scale, making short work of calculating totals and averages of thousands or millions of values. Humans are still better than AI at certain kinds of complex tasks when those tasks are performed on a small scale.</p><p>Amazon’s Alexa, Apple’s Siri, and Google’s Assistant use a special kind of AI, Natural Language Processing (NLP), to recognize spoken commands and questions, and to provide synthetic replies. As complex as these tasks might be, they are not as complex as human conversation which considers not only words and grammar, but tone, and non-verbal and paraverbal cues. Human conversations take place in multiple simultaneous contexts. Words and grammar mean different things in different conversations. Thus far, NLP allows machines to process language. It does not yet enable them to converse as humans do, bringing feelings, perceptions, and imagination into the exchange of information. On the other hand, humans can converse with only a limited number of other humans at once at a limited pace. On the other hand, a machine’s ability to tirelessly process long streams of natural language, even more than one at a time, is limited only by the technical architecture of the hardware and software on which it is realized.</p><p>Figure 1 positions some illustrative problem on axes of scale and complexity. The promise of AI is in the high-complexity, low-scale and high-complexity, high-scale quadrants.</p><p><img src=\"/sites/default/files/inline-images/scale-and-complexity-1.jpg\" data-entity-uuid=\"811a7876-8da6-4989-8474-f27eaee5dfc1\" data-entity-type=\"file\" alt=\" promise of AI\" width=\"500\" height=\"445\"></p><p>This is what makes widespread applications of AI possible – exponential increases in ubiquitous computing capacity. Computers which filled entire rooms in the 1960s were capable of little more than arithmetic at scale. Today, billions of mobile phones contain the computing capacity required to perform the complex tasks which we refer to as Artificial Intelligence. Except where human “wiring” remains the better tool for some kinds of problem-solving, machines can outperform humans in both scale and complexity. This may seem obvious, but it is exactly the point of all technology. The first sharpened stone was a better cutting instrument than human fingernails. This is what we humans do. We are toolmakers. We create things that allow us to solve problems and create value better, faster, and more reliably than we can with our innate capacities. AI is just a new tool. It may be an exceptionally interesting tool because, more than a sharpened stone, we see a bit of ourselves reflected in it.</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstrem Enterprise Applications”, Picirillo, D. 2020.<a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020/\">https://www.wcsaglobal.org/volume-1-issue-2-2020</a></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,78,203,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>Leaders and managers of businesses, non-profit organizations, government agencies and others have something in common. They have organizational goals and organizational stakeholders. Whether those stakeholders are owners, customers, social service clients, or citizen constituents, the organizational goals must align in whole or in part with solving a problem or creating something of value for stakeholders. Ultimately, as AI becomes widely adopted, everyone stands to benefit. We are all consumers, citizens, and owners. We are all stakeholders.</p><p>Here again some definitions are necessary. By “enterprise systems”, we mean solutions built of software applications, data, and supporting technologies which enable an organization to achieve its distinct goals and create value for its stakeholders. But we also include in this definition of system, the humans who use the system, the humans who derive some benefit from it, and the policies and operating procedures which govern the use of the technical solution. By “mainstream”, we mean those solutions that are widely adopted or that have the potential for wide adoption yielding transformed operational processes across industry sectors.</p><ul><li>The field of artificial intelligence (AI) is finally yielding valuable smart devices and applications that do more than win games against human champions. According to a report from the Frederick S. Pardee Center for International Futures at the University of Denver, the products of AI are changing the competitive landscape in several industry sectors and are poised to upend operations in many business functions« (Kiron, 2017).</li></ul><p>It is tempting to omit computer games from this discussion. We might not think that they create real value for the stakeholders. But the consumers who pay for them clearly find value in them as a form of entertainment. Further, the broad genre of computer applications, games and simulations, which incorporate advanced AI technologies, create stakeholder value as training and development platforms. One need to look no further than commercial flight simulators which allow students and pilots of all levels of experience to train and learn without the risk of making inappropriate contact with the ground in a real aircraft with real people aboard.</p><p>For over half a century, enterprises of all kinds have adopted successive generations of information technology to create stakeholder value by increasing sales, reducing costs, producing widgets, or delivering services faster and more reliably. Organizations of all sizes today use a computer of some kind to house their Systems of Record – a system for the collection and management of transactional information, for operational needs and management reporting. Some organizations find ways to achieve competitive advantage through clever uses of Information Technology, often doing the same thing as their competitors, just doing it faster or cheaper. We can call this a System of Differentiation. A few companies change the marketplace through Systems of Innovation – innovative applications of Information Technology. A leading Information Technology research firm introduced this three-layer model in 2012, referring to it as “Pace Layered Application Strategy” (Gartner, 2012) because each layer promises to drive organizational change and accelerate value creation at a different pace.</p><p>The most well-known example of Systems of Innovation is an entire enterprise – Amazon.com. Beginning as an online bookseller, Amazon innovated so profoundly that they almost single-handedly killed brick and mortar bookstores. It is telling that they are not known as Amazon, Incorporated, or the Amazon Company. They are known by their Internet domain name. Today, their technology innovations have not only transformed how households and business acquire all manner of goods, their technology itself has become a market-disruptive service in the form of Amazon Web Services (AWS). Further, AWS, along with Microsoft Azure, IBM Watson, and others are now commoditizing elements of their AI infrastructure providing an unprecedented opportunity for enterprises which are not among the “tech giants’ to employ these technologies in their pursuit of stakeholder value.</p><p>Table 1 lists a sample of the AI services provided by AI that enterprises can integrate into their systems. According to Amazon, “you get instant access to fast, high quality AI tools based on the same technology used to power Amazon’s own businesses.” (AWS, 2021)</p><p><strong>Table 1: Sample AWS AI Services (source: AWS, 2021)</strong></p><table border=\"1\" cellspacing=\"0\" cellpadding=\"8\"><thead><tr><th>Service Name</th><th>Service Description</th></tr></thead><tbody><tr><td>Amazon CodeGuru</td><td>Automate code reviews to improve software quality</td></tr><tr><td>Amazon Comprehend</td><td>Discover insights and relationships in text</td></tr><tr><td>Amazon Fraud Detector</td><td>Detect online fraud faster</td></tr><tr><td>Amazon Lex</td><td>Build voice and text chatbots</td></tr><tr><td>Amazon Polly</td><td>Turn text into lifelike speech</td></tr><tr><td>Amazon Recognition</td><td>Analyze image and video</td></tr><tr><td>Amazon Transcribe</td><td>Automatic speech recognition</td></tr></tbody></table><p>Table 2 lists some of Microsoft’s AI services provide under a product line called Azure Cognitive Services.</p><p><strong>Table 2: Sample Microsoft AI Services (Microsoft Azure Cognitive Services, 2021)</strong></p><table border=\"1\" cellspacing=\"0\" cellpadding=\"8\"><thead><tr><th>Service Name</th><th>Service Description</th></tr></thead><tbody><tr><td>Content Moderator</td><td>Detect potentially offensive or unwanted content</td></tr><tr><td>Text Analytics</td><td>Detect sentiment, key phrases, and named entities</td></tr><tr><td>Translator</td><td>Detect and translate more than 60 supported languages</td></tr><tr><td>Speaker Recognition</td><td>Identify and verify the people speaking based on audio</td></tr><tr><td>Computer Vision</td><td>Analyze content images</td></tr><tr><td>Face</td><td>Detect and identify people and emotions in images</td></tr><tr><td>Video Indexer</td><td>Analyze the visual and audio channels of a video, and index its content</td></tr></tbody></table>','','full_html'),('blog',0,78,204,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>Leaders and managers of businesses, non-profit organizations, government agencies and others have something in common. They have organizational goals and organizational stakeholders. Whether those stakeholders are owners, customers, social service clients, or citizen constituents, the organizational goals must align in whole or in part with solving a problem or creating something of value for stakeholders. Ultimately, as AI becomes widely adopted, everyone stands to benefit. We are all consumers, citizens, and owners. We are all stakeholders.</p><p>Here again some definitions are necessary. By “enterprise systems”, we mean solutions built of software applications, data, and supporting technologies which enable an organization to achieve its distinct goals and create value for its stakeholders. But we also include in this definition of system, the humans who use the system, the humans who derive some benefit from it, and the policies and operating procedures which govern the use of the technical solution. By “mainstream”, we mean those solutions that are widely adopted or that have the potential for wide adoption yielding transformed operational processes across industry sectors.</p><ul><li>The field of artificial intelligence (AI) is finally yielding valuable smart devices and applications that do more than win games against human champions. According to a report from the Frederick S. Pardee Center for International Futures at the University of Denver, the products of AI are changing the competitive landscape in several industry sectors and are poised to upend operations in many business functions« (Kiron, 2017).</li></ul><p>It is tempting to omit computer games from this discussion. We might not think that they create real value for the stakeholders. But the consumers who pay for them clearly find value in them as a form of entertainment. Further, the broad genre of computer applications, games and simulations, which incorporate advanced AI technologies, create stakeholder value as training and development platforms. One need to look no further than commercial flight simulators which allow students and pilots of all levels of experience to train and learn without the risk of making inappropriate contact with the ground in a real aircraft with real people aboard.</p><p>For over half a century, enterprises of all kinds have adopted successive generations of information technology to create stakeholder value by increasing sales, reducing costs, producing widgets, or delivering services faster and more reliably. Organizations of all sizes today use a computer of some kind to house their Systems of Record – a system for the collection and management of transactional information, for operational needs and management reporting. Some organizations find ways to achieve competitive advantage through clever uses of Information Technology, often doing the same thing as their competitors, just doing it faster or cheaper. We can call this a System of Differentiation. A few companies change the marketplace through Systems of Innovation – innovative applications of Information Technology. A leading Information Technology research firm introduced this three-layer model in 2012, referring to it as “Pace Layered Application Strategy” (Gartner, 2012) because each layer promises to drive organizational change and accelerate value creation at a different pace.</p><p>The most well-known example of Systems of Innovation is an entire enterprise – Amazon.com. Beginning as an online bookseller, Amazon innovated so profoundly that they almost single-handedly killed brick and mortar bookstores. It is telling that they are not known as Amazon, Incorporated, or the Amazon Company. They are known by their Internet domain name. Today, their technology innovations have not only transformed how households and business acquire all manner of goods, their technology itself has become a market-disruptive service in the form of Amazon Web Services (AWS). Further, AWS, along with Microsoft Azure, IBM Watson, and others are now commoditizing elements of their AI infrastructure providing an unprecedented opportunity for enterprises which are not among the “tech giants’ to employ these technologies in their pursuit of stakeholder value.</p><p>Table 1 lists a sample of the AI services provided by AI that enterprises can integrate into their systems. According to Amazon, “you get instant access to fast, high quality AI tools based on the same technology used to power Amazon’s own businesses.” (AWS, 2021)</p><p><strong>Table 1: Sample AWS AI Services (source: AWS, 2021)</strong></p><table border=\"1\" cellspacing=\"0\" cellpadding=\"8\"><thead><tr><th>Service Name</th><th>Service Description</th></tr></thead><tbody><tr><td>Amazon CodeGuru</td><td>Automate code reviews to improve software quality</td></tr><tr><td>Amazon Comprehend</td><td>Discover insights and relationships in text</td></tr><tr><td>Amazon Fraud Detector</td><td>Detect online fraud faster</td></tr><tr><td>Amazon Lex</td><td>Build voice and text chatbots</td></tr><tr><td>Amazon Polly</td><td>Turn text into lifelike speech</td></tr><tr><td>Amazon Recognition</td><td>Analyze image and video</td></tr><tr><td>Amazon Transcribe</td><td>Automatic speech recognition</td></tr></tbody></table><p>Table 2 lists some of Microsoft’s AI services provide under a product line called Azure Cognitive Services.</p><p><strong>Table 2: Sample Microsoft AI Services (Microsoft Azure Cognitive Services, 2021)</strong></p><table border=\"1\" cellspacing=\"0\" cellpadding=\"8\"><thead><tr><th>Service Name</th><th>Service Description</th></tr></thead><tbody><tr><td>Content Moderator</td><td>Detect potentially offensive or unwanted content</td></tr><tr><td>Text Analytics</td><td>Detect sentiment, key phrases, and named entities</td></tr><tr><td>Translator</td><td>Detect and translate more than 60 supported languages</td></tr><tr><td>Speaker Recognition</td><td>Identify and verify the people speaking based on audio</td></tr><tr><td>Computer Vision</td><td>Analyze content images</td></tr><tr><td>Face</td><td>Detect and identify people and emotions in images</td></tr><tr><td>Video Indexer</td><td>Analyze the visual and audio channels of a video, and index its content</td></tr></tbody></table><p>With access to exponentially more powerful computing capabilities and the capacity to cost-effectively generate and store vast amounts of data many organizations are just trying to make sense of the ever-increasing waves of transactional data created by operational activity. Even so, the goal posts keep moving. Organizations must do more than simply keep up with volume. They must find differentiating and innovative ways to extract value from the data, to intelligently steer the enterprise, one operational process at a time, through Systems of Intelligence.</p><p>Systems of Intelligence is a fourth layer not explicitly anticipated in the 2012 Gartner report in the sense that it offers the potential of something beyond what we traditionally call innovation. In Gartner’s Pace Layers, we think of innovation as something humans do, enabled by technology. The human has a hypothesis about the market or a particular kind of stakeholder. A System of Innovation, relying on machine learning still depends on a hypothesis formed by a human, but it offers the promise of validating or invalidating many hypotheses, quickly, throughout a complex enterprise.</p><p>Data warehouse analytics was once called innovative. Today, the ability to process massive amounts of data and produce meaningful insights are a mature technology – mature enough to be called “traditional”. These technologies can create significant stakeholder value, but they suffer from a fundamental limitation. They are all backward facing. Industry research analyst David Floyer said this, “Traditional Data Warehouse Analytics empower the few to steer the ship by analyzing its wake.” (Floyer, 2015) Traditional analytics most often deliver value as hindsight. Predictive analytics attempt to add value as foresight. But the accuracy of predictions can be diminished by age of the data. Analyses are often based on data which is days or weeks old. Results are published in reports and dashboard to managers and executives who then decide what to share. In contrast, the vision for Systems of Intelligence is real-time, or near real-time analyses, recommendations, and decisions, in the hands of the information producer and consumer. The idea is, process as much data as possible, as quickly as possible, with the best available algorithms to steer the ship intelligently, collectively, and organically with a forward outlook.</p><p>Machine Learning often dominates discussions about AI in which the topic is novel problem solving and modeling and deriving insights from massive and complex data. In the potential for widespread enterprise adoption however, several kinds of AI have the potential to create stakeholder value, separately or in combination.</p><ul><li>Natural Language Processing</li><li>Image Recognition</li><li>Machine Learning based analysis and forecasting</li><li>Robotics</li></ul><p>This is not an exhaustive list, but these four kinds of AI are relatively easy to define empirically through well-known examples. Stanford’s One Hundred Year Study on Artificial Intelligence — <a href=\"https://ai100.stanford.edu/2016-report\">Artificial Intelligence and Life in 2030</a>— lists 11 applications of AI each of which are forms of narrow AI. Some of the most compelling applications, or use cases, employ more than one kind of AI. For reasons of space and simplicity, we will go only a little deeper into the found kinds of AI mentioned above.</p><p>Natural Language Processing (NLP) is the ability of a machine to process human communication in its natural form, written or audible, and generate human-understandable responses in a natural form. Apple’s Siri, Google’s Assistant, and Amazon Alexa are well known examples of the tech giants. Many other enterprises are today using some form of NLP. Airline reservation systems are a prominent example. In this case, the organization derives stakeholder value by providing customer service for routine matters at a lower cost than doing so with human agents.</p><p>Image Recognition appears in Google’s reverse image search at images.google.com. Instead of typing a search term and receiving images as result, a user can provide an image and the technology tells you what it probably is, where to read about it and a selection of visually similar images.</p><p>The photo in Figure 2 (taken by the author in February 2020) returned the primary result “Western Wall” even though the most visually prominent object is not the wall but the Dome of the Rock and other structures beyond the wall. A review of the visually similar images suggests an explanation. Many of the visually similar images were captured from the same direction but with a wider view. In them, the Western Wall is visible along with the Dome of the Rock. The images appear to have a relatively larger number of metadata tags which refer to the Western Wall rather than the Dome of the Rock. This reveals potential weakness of image recognition technologies. All AI must be “trained” in some way. That is, we give the machine examples and tell it how to recognize important things in the examples. In this instance of image recognition, users contribute images and add keywords or metadata about the image. Because many images in the training data containing the Dome of the Rock are tagged with keywords referring to the Western Wall, Google’s algorithm says this is a photo of the wall, even though the wall is not visible. This answer is in the right neighborhood, in more ways than one, but it is not correct. A human observer would say this is a photo of the Dome of the Rock, not of the Western Wall. As impressive as the technology has become, it is not able to overcome poor training or a bias in the training data. Because of errors like this, humans need to supervise the machine’s output, especially when that output is used to inform high-stakes decisions. With enough correction provided by human supervisors, the machine will learn more and produce increasingly accurate results.</p><p>&nbsp;</p><p><img src=\"/sites/default/files/inline-images/Jerusalem.jpg\" data-entity-uuid=\"ed988183-66fe-41f3-9cae-a39dba82e20c\" data-entity-type=\"file\" width=\"468\" height=\"312\"></p><p>Figure 2: The Dome of the Rock, Jerusalem (Source: the author)</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstrem Enterprise Applications”, Picirillo, D. 2020. <a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020\">https://www.wcsaglobal.org/volume-1-issue-2-2020/</a></p><p><strong>References</strong></p><p>AWS (Amazon Web Services), 2021, Machine Learing on AWS, June 7, 2021. <a href=\"https://aws.amazon.com/ai/machine-learning/\">https://aws.amazon.com/machine-learning</a></p><p>Deloitte. 2020. Industry 4.0, Deloitte Insights <a href=\"https://www2.deloitte.com/us/en/insights/focus/industry-4-0.html\">https://www2.deloitte.com/us/en/insights/focus/industry-4-0.html</a></p><p>Gardner H., 1983, Frames of Mind: The Theory of Multiple Intelligences. New York, Fontana Press.</p><p>Floyer D., 2015, Managing the Data Tsunami with Systems of Intelligence. Wikibon, Inc. October 10, 2015. <a href=\"https://wikibon.com/managing-the-data-tsunami-with-systems-of-intelligence\">https://wikibon.com/managing-the-data-tsunami-with-systems-of-intelligence</a></p><p>Gaughan D., 2012, Introducing the Pace Layered Application Strategy Special Report. Gartner Inc., February 3, 2012. <a href=\"https://blogs.gartner.com/dennis-gaughan/2012/02/03/introducing-the-pace-layered-application-strategy-special-report\">https://blogs.gartner.com/dennis-gaughan/2012/02/03/introducing-the-pace-layered-application-strategy-special-report</a></p><p>Hickey J., 2020, Using Machine Learning to “Nowcast” Precipitation in High Resolution. January 13, 2020. Google. <a href=\"https://ai.googleblog.com/2020/01/using-machine-learning-to-nowcast.html\">https://ai.googleblog.com/2020/01/using-machine-learning-to-nowcast.html</a></p><p>King S., 2020, Why COVID-19 Is Accelerating The Adoption Of AI And Research Tech. May 11, 2020. Forbes. <a href=\"https://www.forbes.com/sites/steveking/2020/05/11/why-covid-19-is-accelerating-the-adoption-of-ai-and-research-tech/#2983b7162140\">https://www.forbes.com/sites/steveking/2020/05/11/why-covid-19-is-accelerating-the-adoption-of-ai-and-research-tech/#2983b7162140</a></p><p>Microsoft, 2021, Microsoft Azure Cognitive Services, June 7, 2021. <a href=\"https://azure.microsoft.com/en-us/services/cognitive-services\">https://azure.microsoft.com/en-us/services/cognitive-services</a></p><p>Stone P., Brooks R., Brynjolfsson E., Calo R., Etzioni O., Hager G., Hirschberg J., Kalyanakrishnan S., Kamar E., Kraus S., Leyton-Brown K., Parkes D., Press W., Saxenian A., Shah J., Tambe M., and Teller A., 2016. Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016.<a href=\"http://ai100.stanford.edu/2016-report\">http://ai100.stanford.edu/2016-report</a></p><p><strong>Posts in this series</strong></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html'),('blog',0,78,241,'en',0,'<p>This article is one of a series on the subject of Artificial Intelligence in mainstream enterprise computing.</p><p>Leaders and managers of businesses, non-profit organizations, government agencies and others have something in common. They have organizational goals and organizational stakeholders. Whether those stakeholders are owners, customers, social service clients, or citizen constituents, the organizational goals must align in whole or in part with solving a problem or creating something of value for stakeholders. Ultimately, as AI becomes widely adopted, everyone stands to benefit. We are all consumers, citizens, and owners. We are all stakeholders.</p><p>Here again some definitions are necessary. By “enterprise systems”, we mean solutions built of software applications, data, and supporting technologies which enable an organization to achieve its distinct goals and create value for its stakeholders. But we also include in this definition of system, the humans who use the system, the humans who derive some benefit from it, and the policies and operating procedures which govern the use of the technical solution. By “mainstream”, we mean those solutions that are widely adopted or that have the potential for wide adoption yielding transformed operational processes across industry sectors.</p><ul><li>The field of artificial intelligence (AI) is finally yielding valuable smart devices and applications that do more than win games against human champions. According to a report from the Frederick S. Pardee Center for International Futures at the University of Denver, the products of AI are changing the competitive landscape in several industry sectors and are poised to upend operations in many business functions« (Kiron, 2017).</li></ul><p>It is tempting to omit computer games from this discussion. We might not think that they create real value for the stakeholders. But the consumers who pay for them clearly find value in them as a form of entertainment. Further, the broad genre of computer applications, games and simulations, which incorporate advanced AI technologies, create stakeholder value as training and development platforms. One need to look no further than commercial flight simulators which allow students and pilots of all levels of experience to train and learn without the risk of making inappropriate contact with the ground in a real aircraft with real people aboard.</p><p>For over half a century, enterprises of all kinds have adopted successive generations of information technology to create stakeholder value by increasing sales, reducing costs, producing widgets, or delivering services faster and more reliably. Organizations of all sizes today use a computer of some kind to house their Systems of Record – a system for the collection and management of transactional information, for operational needs and management reporting. Some organizations find ways to achieve competitive advantage through clever uses of Information Technology, often doing the same thing as their competitors, just doing it faster or cheaper. We can call this a System of Differentiation. A few companies change the marketplace through Systems of Innovation – innovative applications of Information Technology. A leading Information Technology research firm introduced this three-layer model in 2012, referring to it as “Pace Layered Application Strategy” (Gartner, 2012) because each layer promises to drive organizational change and accelerate value creation at a different pace.</p><p>The most well-known example of Systems of Innovation is an entire enterprise – Amazon.com. Beginning as an online bookseller, Amazon innovated so profoundly that they almost single-handedly killed brick and mortar bookstores. It is telling that they are not known as Amazon, Incorporated, or the Amazon Company. They are known by their Internet domain name. Today, their technology innovations have not only transformed how households and business acquire all manner of goods, their technology itself has become a market-disruptive service in the form of Amazon Web Services (AWS). Further, AWS, along with Microsoft Azure, IBM Watson, and others are now commoditizing elements of their AI infrastructure providing an unprecedented opportunity for enterprises which are not among the “tech giants’ to employ these technologies in their pursuit of stakeholder value.</p><p>Table 1 lists a sample of the AI services provided by AI that enterprises can integrate into their systems. According to Amazon, “you get instant access to fast, high quality AI tools based on the same technology used to power Amazon’s own businesses.” (AWS, 2021)</p><p><strong>Table 1: Sample AWS AI Services (source: AWS, 2021)</strong></p><table border=\"1\" cellspacing=\"0\" cellpadding=\"8\"><thead><tr><th>Service Name</th><th>Service Description</th></tr></thead><tbody><tr><td>Amazon CodeGuru</td><td>Automate code reviews to improve software quality</td></tr><tr><td>Amazon Comprehend</td><td>Discover insights and relationships in text</td></tr><tr><td>Amazon Fraud Detector</td><td>Detect online fraud faster</td></tr><tr><td>Amazon Lex</td><td>Build voice and text chatbots</td></tr><tr><td>Amazon Polly</td><td>Turn text into lifelike speech</td></tr><tr><td>Amazon Recognition</td><td>Analyze image and video</td></tr><tr><td>Amazon Transcribe</td><td>Automatic speech recognition</td></tr></tbody></table><p>Table 2 lists some of Microsoft’s AI services provide under a product line called Azure Cognitive Services.</p><p><strong>Table 2: Sample Microsoft AI Services (Microsoft Azure Cognitive Services, 2021)</strong></p><table border=\"1\" cellspacing=\"0\" cellpadding=\"8\"><thead><tr><th>Service Name</th><th>Service Description</th></tr></thead><tbody><tr><td>Content Moderator</td><td>Detect potentially offensive or unwanted content</td></tr><tr><td>Text Analytics</td><td>Detect sentiment, key phrases, and named entities</td></tr><tr><td>Translator</td><td>Detect and translate more than 60 supported languages</td></tr><tr><td>Speaker Recognition</td><td>Identify and verify the people speaking based on audio</td></tr><tr><td>Computer Vision</td><td>Analyze content images</td></tr><tr><td>Face</td><td>Detect and identify people and emotions in images</td></tr><tr><td>Video Indexer</td><td>Analyze the visual and audio channels of a video, and index its content</td></tr></tbody></table><p>With access to exponentially more powerful computing capabilities and the capacity to cost-effectively generate and store vast amounts of data many organizations are just trying to make sense of the ever-increasing waves of transactional data created by operational activity. Even so, the goal posts keep moving. Organizations must do more than simply keep up with volume. They must find differentiating and innovative ways to extract value from the data, to intelligently steer the enterprise, one operational process at a time, through Systems of Intelligence.</p><p>Systems of Intelligence is a fourth layer not explicitly anticipated in the 2012 Gartner report in the sense that it offers the potential of something beyond what we traditionally call innovation. In Gartner’s Pace Layers, we think of innovation as something humans do, enabled by technology. The human has a hypothesis about the market or a particular kind of stakeholder. A System of Innovation, relying on machine learning still depends on a hypothesis formed by a human, but it offers the promise of validating or invalidating many hypotheses, quickly, throughout a complex enterprise.</p><p>Data warehouse analytics was once called innovative. Today, the ability to process massive amounts of data and produce meaningful insights are a mature technology – mature enough to be called “traditional”. These technologies can create significant stakeholder value, but they suffer from a fundamental limitation. They are all backward facing. Industry research analyst David Floyer said this, “Traditional Data Warehouse Analytics empower the few to steer the ship by analyzing its wake.” (Floyer, 2015) Traditional analytics most often deliver value as hindsight. Predictive analytics attempt to add value as foresight. But the accuracy of predictions can be diminished by age of the data. Analyses are often based on data which is days or weeks old. Results are published in reports and dashboard to managers and executives who then decide what to share. In contrast, the vision for Systems of Intelligence is real-time, or near real-time analyses, recommendations, and decisions, in the hands of the information producer and consumer. The idea is, process as much data as possible, as quickly as possible, with the best available algorithms to steer the ship intelligently, collectively, and organically with a forward outlook.</p><p>Machine Learning often dominates discussions about AI in which the topic is novel problem solving and modeling and deriving insights from massive and complex data. In the potential for widespread enterprise adoption however, several kinds of AI have the potential to create stakeholder value, separately or in combination.</p><ul><li>Natural Language Processing</li><li>Image Recognition</li><li>Machine Learning based analysis and forecasting</li><li>Robotics</li></ul><p>This is not an exhaustive list, but these four kinds of AI are relatively easy to define empirically through well-known examples. Stanford’s One Hundred Year Study on Artificial Intelligence — <a href=\"https://ai100.stanford.edu/2016-report\">Artificial Intelligence and Life in 2030</a>— lists 11 applications of AI each of which are forms of narrow AI. Some of the most compelling applications, or use cases, employ more than one kind of AI. For reasons of space and simplicity, we will go only a little deeper into the found kinds of AI mentioned above.</p><p>Natural Language Processing (NLP) is the ability of a machine to process human communication in its natural form, written or audible, and generate human-understandable responses in a natural form. Apple’s Siri, Google’s Assistant, and Amazon Alexa are well known examples of the tech giants. Many other enterprises are today using some form of NLP. Airline reservation systems are a prominent example. In this case, the organization derives stakeholder value by providing customer service for routine matters at a lower cost than doing so with human agents.</p><p>Image Recognition appears in Google’s reverse image search at images.google.com. Instead of typing a search term and receiving images as result, a user can provide an image and the technology tells you what it probably is, where to read about it and a selection of visually similar images.</p><p>The photo in Figure 2 (taken by the author in February 2020) returned the primary result “Western Wall” even though the most visually prominent object is not the wall but the Dome of the Rock and other structures beyond the wall. A review of the visually similar images suggests an explanation. Many of the visually similar images were captured from the same direction but with a wider view. In them, the Western Wall is visible along with the Dome of the Rock. The images appear to have a relatively larger number of metadata tags which refer to the Western Wall rather than the Dome of the Rock. This reveals potential weakness of image recognition technologies. All AI must be “trained” in some way. That is, we give the machine examples and tell it how to recognize important things in the examples. In this instance of image recognition, users contribute images and add keywords or metadata about the image. Because many images in the training data containing the Dome of the Rock are tagged with keywords referring to the Western Wall, Google’s algorithm says this is a photo of the wall, even though the wall is not visible. This answer is in the right neighborhood, in more ways than one, but it is not correct. A human observer would say this is a photo of the Dome of the Rock, not of the Western Wall. As impressive as the technology has become, it is not able to overcome poor training or a bias in the training data. Because of errors like this, humans need to supervise the machine’s output, especially when that output is used to inform high-stakes decisions. With enough correction provided by human supervisors, the machine will learn more and produce increasingly accurate results.</p><p>&nbsp;</p><p><img src=\"/sites/default/files/inline-images/Jerusalem.jpg\" data-entity-uuid=\"ed988183-66fe-41f3-9cae-a39dba82e20c\" data-entity-type=\"file\" width=\"468\" height=\"312\"></p><p>Figure 2: The Dome of the Rock, Jerusalem (Source: the author)</p><p>Portions of this article originally appeared in the Journal of the World Complexity Science Academy Volume 1, Issue 2 as “Artificial Intelligence in Mainstrem Enterprise Applications”, Picirillo, D. 2020. <a href=\"https://www.wcsaglobal.org/volume-1-issue-2-2020\">https://www.wcsaglobal.org/volume-1-issue-2-2020/</a></p><p><strong>References</strong></p><p>AWS (Amazon Web Services), 2021, Machine Learing on AWS, June 7, 2021. <a href=\"https://aws.amazon.com/ai/machine-learning/\">https://aws.amazon.com/machine-learning</a></p><p>Deloitte. 2020. Industry 4.0, Deloitte Insights <a href=\"https://www2.deloitte.com/us/en/insights/focus/industry-4-0.html\">https://www2.deloitte.com/us/en/insights/focus/industry-4-0.html</a></p><p>Gardner H., 1983, Frames of Mind: The Theory of Multiple Intelligences. New York, Fontana Press.</p><p>Floyer D., 2015, Managing the Data Tsunami with Systems of Intelligence. Wikibon, Inc. October 10, 2015. <a href=\"https://wikibon.com/managing-the-data-tsunami-with-systems-of-intelligence\">https://wikibon.com/managing-the-data-tsunami-with-systems-of-intelligence</a></p><p>Gaughan D., 2012, Introducing the Pace Layered Application Strategy Special Report. Gartner Inc., February 3, 2012. <a href=\"https://blogs.gartner.com/dennis-gaughan/2012/02/03/introducing-the-pace-layered-application-strategy-special-report\">https://blogs.gartner.com/dennis-gaughan/2012/02/03/introducing-the-pace-layered-application-strategy-special-report</a></p><p>Hickey J., 2020, Using Machine Learning to “Nowcast” Precipitation in High Resolution. January 13, 2020. Google. <a href=\"https://ai.googleblog.com/2020/01/using-machine-learning-to-nowcast.html\">https://ai.googleblog.com/2020/01/using-machine-learning-to-nowcast.html</a></p><p>King S., 2020, Why COVID-19 Is Accelerating The Adoption Of AI And Research Tech. May 11, 2020. Forbes. <a href=\"https://www.forbes.com/sites/steveking/2020/05/11/why-covid-19-is-accelerating-the-adoption-of-ai-and-research-tech/#2983b7162140\">https://www.forbes.com/sites/steveking/2020/05/11/why-covid-19-is-accelerating-the-adoption-of-ai-and-research-tech/#2983b7162140</a></p><p>Microsoft, 2021, Microsoft Azure Cognitive Services, June 7, 2021. <a href=\"https://azure.microsoft.com/en-us/services/cognitive-services\">https://azure.microsoft.com/en-us/services/cognitive-services</a></p><p>Stone P., Brooks R., Brynjolfsson E., Calo R., Etzioni O., Hager G., Hirschberg J., Kalyanakrishnan S., Kamar E., Kraus S., Leyton-Brown K., Parkes D., Press W., Saxenian A., Shah J., Tambe M., and Teller A., 2016. Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016.<a href=\"http://ai100.stanford.edu/2016-report\">http://ai100.stanford.edu/2016-report</a></p><p><strong>Posts in this series</strong></p><ol><li>AI In Mainstream Enterprise Applications</li><li>What does AI offer humankind?</li><li>What Does AI Offer Users Of Mainstream Enterprise Systems?</li></ol>','','full_html');

INSERT INTO `node_revision__body` VALUES ('blog',0,89,489,'en',0,'<p>My blog posts here mostly focus on the complexity of systems supporting business operations, the complicating factors of our reliance on the internet for business communications and commerce, the extended risk and value chain of multiple layers of suppliers and customers engaged with our businesses, and the roles and methods of managing information security management in large and small interconnected businesses. <strong>Today I am going to focus narrowly on the role of the Chief Information Security Officer (CISO).</strong></p><p>I have been engaged in information technology for enough years to have witnessed the birth of the role of the Chief Information Security Officer. In the early days of my career, we did not have such a role in an IT organization. We had roles for Security Managers reporting to the Chief Information Officer (CIO) or Chief Technology Officer (CTO) and even lower in the hierarchy of Information Technology organizational structures. Indeed, I was once designated Security Manager for a large aerospace company back in the long-ago times of the 1980’s. In another stage of my career, I served as an SVP level Chief Security Officer for a large multi-national outsourcing firm with multiple CISOs and Security Officers (SOs) reporting to me. The reason I am turning to the CISO role today is that I recently read an article which declared that:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>Quite a striking comment, especially if you hold a CISO title in an organization today. Normally when I include someone else’s words in one of my posts I include an attribution to that person as the source or author. In this case the article was attributed to an online forum for a for profit company, not an individual author, and I have chosen not to provide pro bono advertising.</p><p>When I read this comment, it really caught my attention.<strong> “The CISO role is dead” </strong>is a strong statement which resonates with what I am seeing and experiencing today in many organizations. Maybe the CISO role has outlived its purpose or is now facing a need to evolve.</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my +45 years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Maybe it is time for the CISO role to evolve or morph into something new, a role which truly addresses the need for a holistic approach to managing systemic information risk inherent in our complex digital systems. If so, what should this new role look like? Is it time to break out of the Information Technology organizational driven focus of Information Security and elevate a company’s focus on and treatment of Information Security Risk? Should this role be a part of an Information Technology function, or should it be part of a broader approach to managing business risk and reside elsewhere in a business organization?</p><p><em><strong>IT Risk = Business Risk</strong></em></p><p>If we accept that all IT risk is Business Risk, then we should logically accept that managing information risk should not reside solely within an information technology organization. Thus, I propose that two changes are needed to adequately address the inherent systemic risk in our complex digital systems. The first is a new role. The second is that the new role should not be placed inside the Information Technology organization.</p><p><strong>Digital Risk and Resilience Officer</strong></p><p>This role, Digital Risk and Resilience Officer (DRRO), should be part of a business’s Enterprise Risk Management (ERM) function and may or may not exist as the only senior role addressing information risk and security. I envision that many information Technology organizations may elect to maintain a traditional Chief Information Security Officer (CISO) role solely focused internally within the IT organization. If so, I suggest that the role should no longer be called a “Chief” role, but rather be titled as an Information Security Officer role. As many business organizations already embrace Enterprise Risk Management and have a senior leadership role for a Chief Risk Officer (CRO), I envision that the emerging DRRO role would report to the Chief Risk Officer and collaborate with both the CRO and the IT Information Security Officer (ISO). The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day.</p><p>I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>In my next bog post, I will expand on my thoughts concerning a Digital Risk and Resilience Officer role.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,89,569,'en',0,'<p>My blog posts here mostly focus on the complexity of systems supporting business operations, the complicating factors of our reliance on the internet for business communications and commerce, the extended risk and value chain of multiple layers of suppliers and customers engaged with our businesses, and the roles and methods of managing information security management in large and small interconnected businesses. <strong>Today I am going to focus narrowly on the role of the Chief Information Security Officer (CISO).</strong></p><p>I have been engaged in information technology for enough years to have witnessed the birth of the role of the Chief Information Security Officer. In the early days of my career, we did not have such a role in an IT organization. We had roles for Security Managers reporting to the Chief Information Officer (CIO) or Chief Technology Officer (CTO) and even lower in the hierarchy of Information Technology organizational structures. Indeed, I was once designated Security Manager for a large aerospace company back in the long-ago times of the 1980’s. In another stage of my career, I served as an SVP level Chief Security Officer for a large multi-national outsourcing firm with multiple CISOs and Security Officers (SOs) reporting to me. The reason I am turning to the CISO role today is that I recently read an article which declared that:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>Quite a striking comment, especially if you hold a CISO title in an organization today. Normally when I include someone else’s words in one of my posts I include an attribution to that person as the source or author. In this case the article was attributed to an online forum for a for profit company, not an individual author, and I have chosen not to provide pro bono advertising.</p><p>When I read this comment, it really caught my attention.<strong> “The CISO role is dead” </strong>is a strong statement which resonates with what I am seeing and experiencing today in many organizations. Maybe the CISO role has outlived its purpose or is now facing a need to evolve.</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my +45 years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Maybe it is time for the CISO role to evolve or morph into something new, a role which truly addresses the need for a holistic approach to managing systemic information risk inherent in our complex digital systems. If so, what should this new role look like? Is it time to break out of the Information Technology organizational driven focus of Information Security and elevate a company’s focus on and treatment of Information Security Risk? Should this role be a part of an Information Technology function, or should it be part of a broader approach to managing business risk and reside elsewhere in a business organization?</p><p><em><strong>IT Risk = Business Risk</strong></em></p><p>If we accept that all IT risk is Business Risk, then we should logically accept that managing information risk should not reside solely within an information technology organization. Thus, I propose that two changes are needed to adequately address the inherent systemic risk in our complex digital systems. The first is a new role. The second is that the new role should not be placed inside the Information Technology organization.</p><p><strong>Digital Risk and Resilience Officer</strong></p><p>This role, Digital Risk and Resilience Officer (DRRO), should be part of a business’s Enterprise Risk Management (ERM) function and may or may not exist as the only senior role addressing information risk and security. I envision that many information Technology organizations may elect to maintain a traditional Chief Information Security Officer (CISO) role solely focused internally within the IT organization. If so, I suggest that the role should no longer be called a “Chief” role, but rather be titled as an Information Security Officer role. As many business organizations already embrace Enterprise Risk Management and have a senior leadership role for a Chief Risk Officer (CRO), I envision that the emerging DRRO role would report to the Chief Risk Officer and collaborate with both the CRO and the IT Information Security Officer (ISO). The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day.</p><p>I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>In my next bog post, I will expand on my thoughts concerning a Digital Risk and Resilience Officer role.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,89,644,'en',0,'<p>My blog posts here mostly focus on the complexity of systems supporting business operations, the complicating factors of our reliance on the internet for business communications and commerce, the extended risk and value chain of multiple layers of suppliers and customers engaged with our businesses, and the roles and methods of managing information security management in large and small interconnected businesses. <strong>Today I am going to focus narrowly on the role of the Chief Information Security Officer (CISO).</strong></p><p>I have been engaged in information technology for enough years to have witnessed the birth of the role of the Chief Information Security Officer. In the early days of my career, we did not have such a role in an IT organization. We had roles for Security Managers reporting to the Chief Information Officer (CIO) or Chief Technology Officer (CTO) and even lower in the hierarchy of Information Technology organizational structures. Indeed, I was once designated Security Manager for a large aerospace company back in the long-ago times of the 1980’s. In another stage of my career, I served as an SVP level Chief Security Officer for a large multi-national outsourcing firm with multiple CISOs and Security Officers (SOs) reporting to me. The reason I am turning to the CISO role today is that I recently read an article which declared that:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>Quite a striking comment, especially if you hold a CISO title in an organization today. Normally when I include someone else’s words in one of my posts I include an attribution to that person as the source or author. In this case the article was attributed to an online forum for a for profit company, not an individual author, and I have chosen not to provide pro bono advertising.</p><p>When I read this comment, it really caught my attention.<strong> “The CISO role is dead” </strong>is a strong statement which resonates with what I am seeing and experiencing today in many organizations. Maybe the CISO role has outlived its purpose or is now facing a need to evolve.</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my +45 years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Maybe it is time for the CISO role to evolve or morph into something new, a role which truly addresses the need for a holistic approach to managing systemic information risk inherent in our complex digital systems. If so, what should this new role look like? Is it time to break out of the Information Technology organizational driven focus of Information Security and elevate a company’s focus on and treatment of Information Security Risk? Should this role be a part of an Information Technology function, or should it be part of a broader approach to managing business risk and reside elsewhere in a business organization?</p><p><em><strong>IT Risk = Business Risk</strong></em></p><p>If we accept that all IT risk is Business Risk, then we should logically accept that managing information risk should not reside solely within an information technology organization. Thus, I propose that two changes are needed to adequately address the inherent systemic risk in our complex digital systems. The first is a new role. The second is that the new role should not be placed inside the Information Technology organization.</p><p><strong>Digital Risk and Resilience Officer</strong></p><p>This role, Digital Risk and Resilience Officer (DRRO), should be part of a business’s Enterprise Risk Management (ERM) function and may or may not exist as the only senior role addressing information risk and security. I envision that many information Technology organizations may elect to maintain a traditional Chief Information Security Officer (CISO) role solely focused internally within the IT organization. If so, I suggest that the role should no longer be called a “Chief” role, but rather be titled as an Information Security Officer role. As many business organizations already embrace Enterprise Risk Management and have a senior leadership role for a Chief Risk Officer (CRO), I envision that the emerging DRRO role would report to the Chief Risk Officer and collaborate with both the CRO and the IT Information Security Officer (ISO). The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day.</p><p>I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>In my next bog post, I will expand on my thoughts concerning a Digital Risk and Resilience Officer role.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,89,820,'en',0,'<p>My blog posts here mostly focus on the complexity of systems supporting business operations, the complicating factors of our reliance on the internet for business communications and commerce, the extended risk and value chain of multiple layers of suppliers and customers engaged with our businesses, and the roles and methods of managing information security management in large and small interconnected businesses. <strong>Today I am going to focus narrowly on the role of the Chief Information Security Officer (CISO).</strong></p><p>I have been engaged in information technology for enough years to have witnessed the birth of the role of the Chief Information Security Officer. In the early days of my career, we did not have such a role in an IT organization. We had roles for Security Managers reporting to the Chief Information Officer (CIO) or Chief Technology Officer (CTO) and even lower in the hierarchy of Information Technology organizational structures. Indeed, I was once designated Security Manager for a large aerospace company back in the long-ago times of the 1980’s. In another stage of my career, I served as an SVP level Chief Security Officer for a large multi-national outsourcing firm with multiple CISOs and Security Officers (SOs) reporting to me. The reason I am turning to the CISO role today is that I recently read an article which declared that:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>Quite a striking comment, especially if you hold a CISO title in an organization today. Normally when I include someone else’s words in one of my posts I include an attribution to that person as the source or author. In this case the article was attributed to an online forum for a for profit company, not an individual author, and I have chosen not to provide pro bono advertising.</p><p>When I read this comment, it really caught my attention.<strong> “The CISO role is dead” </strong>is a strong statement which resonates with what I am seeing and experiencing today in many organizations. Maybe the CISO role has outlived its purpose or is now facing a need to evolve.</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my +45 years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Maybe it is time for the CISO role to evolve or morph into something new, a role which truly addresses the need for a holistic approach to managing systemic information risk inherent in our complex digital systems. If so, what should this new role look like? Is it time to break out of the Information Technology organizational driven focus of Information Security and elevate a company’s focus on and treatment of Information Security Risk? Should this role be a part of an Information Technology function, or should it be part of a broader approach to managing business risk and reside elsewhere in a business organization?</p><p><em><strong>IT Risk = Business Risk</strong></em></p><p>If we accept that all IT risk is Business Risk, then we should logically accept that managing information risk should not reside solely within an information technology organization. Thus, I propose that two changes are needed to adequately address the inherent systemic risk in our complex digital systems. The first is a new role. The second is that the new role should not be placed inside the Information Technology organization.</p><p><strong>Digital Risk and Resilience Officer</strong></p><p>This role, Digital Risk and Resilience Officer (DRRO), should be part of a business’s Enterprise Risk Management (ERM) function and may or may not exist as the only senior role addressing information risk and security. I envision that many information Technology organizations may elect to maintain a traditional Chief Information Security Officer (CISO) role solely focused internally within the IT organization. If so, I suggest that the role should no longer be called a “Chief” role, but rather be titled as an Information Security Officer role. As many business organizations already embrace Enterprise Risk Management and have a senior leadership role for a Chief Risk Officer (CRO), I envision that the emerging DRRO role would report to the Chief Risk Officer and collaborate with both the CRO and the IT Information Security Officer (ISO). The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day.</p><p>I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>In my next bog post, I will expand on my thoughts concerning a Digital Risk and Resilience Officer role.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,90,223,'en',0,'<p>I began my last blog post by quoting from a business forum I had read:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>And I closed my last post saying that I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>Some of you who read my last post may have dismissed my comment as out of hand. Others may have read a bit deeper into my comments and your reaction, at least internally, were that Joe has no clue what he is talking about. Just maybe, one or two of you may have thought about my comments and said to yourself, he may be onto something.</p><p>I do not claim to have all the clues, but I can assure you that throughout my career I have prided myself in working hard to ensure I had at least half a clue concerning what was going on around me in my professional domain.</p><p>It is a fair bet for me to say again today that:</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my 45+ years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Let me take this one step further:</p><p><em><strong>“Never in the history of modern business operations has there been a risk domain as impactful as Information Technology. Information Technology and Digital Business Systems are the only risk domain which can bring my company’s business operations to a complete standstill in an instant!”</strong></em></p><p>This is a quote from a discussion I recently had with the head of Enterprise Risk Management for a multi-billon dollar business. My conversations during the past year with many Chief Risk Officers, Enterprise Risk Managers and Chief Information Security Officers have all led me to form my opinion that it is time to start thinking about and acting on the notion that the CISO role as we know it today is past its shelf life, and it is time to seriously embrace a Digital Risk and Resilience Officer role.</p><p><strong>The CISO role as we know it today is past its shelf life.</strong></p><p><strong>It is time to embrace the</strong></p><p><strong>Digital Risk and Resilience Officer role.</strong></p><img src=\"/sites/default/files/inline-images/Security%20by%20design%2014.png\" data-entity-uuid=\"02ed5747-2c9b-41b1-8da1-f56c4f693797\" data-entity-type=\"file\" width=\"900\" height=\"394\"><p>&nbsp;</p><p>&nbsp;The traditional CISO role is intended to address the protection of an organization’s information technology, digital business systems, and data from attacks. This is often focused on external threats and can also include controls to address internal misuse of information systems and data. This is a narrow focus given the widespread use of digital systems, data, communications and workplace technologies in use throughout businesses, their customer chains and supply chains. While cybersecurity risk management focuses on cyber threats and recovery of information technology systems, it is a focus which rarely addresses the impacts to the rest of a business organization.</p><p>Enterprises Risk Management acknowledges cybersecurity threats and risks, but sees these risks through a CISO’s eye, usually not through the eyes of your business. I have found it rare when speaking with ERM leaders and Chief Risk Officers to find a fully integrative understanding of the true extent of digital disruption impacts and the need for a business integrative approach to ensure digital resilience when recovering from such business disruptions.</p><p>Another impediment to embracing Digital Resilience, a required new core competence, is what I call grade / position inflation in cybersecurity. Prior to 1995, I do not think there was any role in business for information technology entitled Chief Information Security Officer. There were roles in the 1970’s and 1980’s for information technology security and the role of information technology security manager was common. Steve Katz is credited as the first person appointed to a Chief Information Technology Officer role by Citi Bank in 1995 after Citi Bank suffered a series of successful cyber security attacks by Russian hackers. In the 1990’s and into the early 2000’s it was common to have an information technology security manager role reporting to a Chief Information Officer or a Chief Technology Officer. This role was often filled by a manager or director level position. As the frequency of cybersecurity attacks escalated through the 2000’s, and business impacts became larger, we begin to see the escalation of information technology security managers to higher grade levels. There was something Darwinian about the escalation of security managers to Chief Information Security Managers and further to Director and Officer roles. Today it is not uncommon to see CISO roles as Vice Presidents or Senior Vice Presidents reporting to Chief Information Officers and even to reporting lines directly to business Chief Executive Officers. Raising the position and salary levels of CISOs does not directly translate to significantly improved cybersecurity or digital resilience. Organization performance, in my experience, even for cybersecurity and digital resilience, is primary driven by organizational culture, strong governance, professional competence and employing a right role, right person approach.</p><p><strong>A Very Simple Equation</strong></p><p>My arriving at my realization for a Digital Risk and Resilience Officer role can be summarized as follows:</p><ul><li>Chief Risk Officer is a full-time role / Head of ERM is a full-time role.</li><li>CISO / ISO role is a full-time role.</li><li>CISO/ISO attention to Digital Risk and Resilience is a part-time role.</li><li>Digital Risk and Resilience calls for full-time attention given the potential for catastrophic business disruption impacts.</li><li>Thus … Digital Risk and Resilience should be a full-time role.</li><li>While maintaining the traditional Information Security Officer / Manger role.</li></ul><p><strong>The CISO / ISO role is not dead! It is time to embrace a new and improved role focused on</strong></p><p><strong>Digital Risk and Resilience.</strong></p><p>The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day. I again encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’ future.</p><p>In my next bog post, I will expand on what this might mean to CRO/ERM + DRRO integrative business approach to Enterprise Digital Risk and Resiliency.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,90,231,'en',0,'<p>I began my last blog post by quoting from a business forum I had read:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>And I closed my last post saying that I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>Some of you who read my last post may have dismissed my comment as out of hand. Others may have read a bit deeper into my comments and your reaction, at least internally, were that Joe has no clue what he is talking about. Just maybe, one or two of you may have thought about my comments and said to yourself, he may be onto something.</p><p>I do not claim to have all the clues, but I can assure you that throughout my career I have prided myself in working hard to ensure I had at least half a clue concerning what was going on around me in my professional domain.</p><p>It is a fair bet for me to say again today that:</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my 45+ years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Let me take this one step further:</p><p><em><strong>“Never in the history of modern business operations has there been a risk domain as impactful as Information Technology. Information Technology and Digital Business Systems are the only risk domain which can bring my company’s business operations to a complete standstill in an instant!”</strong></em></p><p>This is a quote from a discussion I recently had with the head of Enterprise Risk Management for a multi-billon dollar business. My conversations during the past year with many Chief Risk Officers, Enterprise Risk Managers and Chief Information Security Officers have all led me to form my opinion that it is time to start thinking about and acting on the notion that the CISO role as we know it today is past its shelf life, and it is time to seriously embrace a Digital Risk and Resilience Officer role.</p><p><strong>The CISO role as we know it today is past its shelf life.</strong></p><p><strong>It is time to embrace the</strong></p><p><strong>Digital Risk and Resilience Officer role.</strong></p><img src=\"/sites/default/files/inline-images/Security%20by%20design%2014.png\" data-entity-uuid=\"02ed5747-2c9b-41b1-8da1-f56c4f693797\" data-entity-type=\"file\" width=\"900\" height=\"394\"><p>&nbsp;</p><p>&nbsp;The traditional CISO role is intended to address the protection of an organization’s information technology, digital business systems, and data from attacks. This is often focused on external threats and can also include controls to address internal misuse of information systems and data. This is a narrow focus given the widespread use of digital systems, data, communications and workplace technologies in use throughout businesses, their customer chains and supply chains. While cybersecurity risk management focuses on cyber threats and recovery of information technology systems, it is a focus which rarely addresses the impacts to the rest of a business organization.</p><p>Enterprises Risk Management acknowledges cybersecurity threats and risks, but sees these risks through a CISO’s eye, usually not through the eyes of your business. I have found it rare when speaking with ERM leaders and Chief Risk Officers to find a fully integrative understanding of the true extent of digital disruption impacts and the need for a business integrative approach to ensure digital resilience when recovering from such business disruptions.</p><p>Another impediment to embracing Digital Resilience, a required new core competence, is what I call grade / position inflation in cybersecurity. Prior to 1995, I do not think there was any role in business for information technology entitled Chief Information Security Officer. There were roles in the 1970’s and 1980’s for information technology security and the role of information technology security manager was common. Steve Katz is credited as the first person appointed to a Chief Information Technology Officer role by Citi Bank in 1995 after Citi Bank suffered a series of successful cyber security attacks by Russian hackers. In the 1990’s and into the early 2000’s it was common to have an information technology security manager role reporting to a Chief Information Officer or a Chief Technology Officer. This role was often filled by a manager or director level position. As the frequency of cybersecurity attacks escalated through the 2000’s, and business impacts became larger, we begin to see the escalation of information technology security managers to higher grade levels. There was something Darwinian about the escalation of security managers to Chief Information Security Managers and further to Director and Officer roles. Today it is not uncommon to see CISO roles as Vice Presidents or Senior Vice Presidents reporting to Chief Information Officers and even to reporting lines directly to business Chief Executive Officers. Raising the position and salary levels of CISOs does not directly translate to significantly improved cybersecurity or digital resilience. Organization performance, in my experience, even for cybersecurity and digital resilience, is primary driven by organizational culture, strong governance, professional competence and employing a right role, right person approach.</p><p><strong>A Very Simple Equation</strong></p><p>My arriving at my realization for a Digital Risk and Resilience Officer role can be summarized as follows:</p><ul><li>Chief Risk Officer is a full-time role / Head of ERM is a full-time role.</li><li>CISO / ISO role is a full-time role.</li><li>CISO/ISO attention to Digital Risk and Resilience is a part-time role.</li><li>Digital Risk and Resilience calls for full-time attention given the potential for catastrophic business disruption impacts.</li><li>Thus … Digital Risk and Resilience should be a full-time role.</li><li>While maintaining the traditional Information Security Officer / Manger role.</li></ul><p><strong>The CISO / ISO role is not dead! It is time to embrace a new and improved role focused on</strong></p><p><strong>Digital Risk and Resilience.</strong></p><p>The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day. I again encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’ future.</p><p>In my next bog post, I will expand on what this might mean to CRO/ERM + DRRO integrative business approach to Enterprise Digital Risk and Resiliency.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,90,232,'en',0,'<p>I began my last blog post by quoting from a business forum I had read:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>And I closed my last post saying that I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>Some of you who read my last post may have dismissed my comment as out of hand. Others may have read a bit deeper into my comments and your reaction, at least internally, were that Joe has no clue what he is talking about. Just maybe, one or two of you may have thought about my comments and said to yourself, he may be onto something.</p><p>I do not claim to have all the clues, but I can assure you that throughout my career I have prided myself in working hard to ensure I had at least half a clue concerning what was going on around me in my professional domain.</p><p>It is a fair bet for me to say again today that:</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my 45+ years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Let me take this one step further:</p><p><em><strong>“Never in the history of modern business operations has there been a risk domain as impactful as Information Technology. Information Technology and Digital Business Systems are the only risk domain which can bring my company’s business operations to a complete standstill in an instant!”</strong></em></p><p>This is a quote from a discussion I recently had with the head of Enterprise Risk Management for a multi-billon dollar business. My conversations during the past year with many Chief Risk Officers, Enterprise Risk Managers and Chief Information Security Officers have all led me to form my opinion that it is time to start thinking about and acting on the notion that the CISO role as we know it today is past its shelf life, and it is time to seriously embrace a Digital Risk and Resilience Officer role.</p><p><strong>The CISO role as we know it today is past its shelf life.</strong></p><p><strong>It is time to embrace the</strong></p><p><strong>Digital Risk and Resilience Officer role.</strong></p><img src=\"/sites/default/files/inline-images/Security%20by%20design%2014.png\" data-entity-uuid=\"02ed5747-2c9b-41b1-8da1-f56c4f693797\" data-entity-type=\"file\" width=\"900\" height=\"394\"><p>&nbsp;</p><p>&nbsp;The traditional CISO role is intended to address the protection of an organization’s information technology, digital business systems, and data from attacks. This is often focused on external threats and can also include controls to address internal misuse of information systems and data. This is a narrow focus given the widespread use of digital systems, data, communications and workplace technologies in use throughout businesses, their customer chains and supply chains. While cybersecurity risk management focuses on cyber threats and recovery of information technology systems, it is a focus which rarely addresses the impacts to the rest of a business organization.</p><p>Enterprises Risk Management acknowledges cybersecurity threats and risks, but sees these risks through a CISO’s eye, usually not through the eyes of your business. I have found it rare when speaking with ERM leaders and Chief Risk Officers to find a fully integrative understanding of the true extent of digital disruption impacts and the need for a business integrative approach to ensure digital resilience when recovering from such business disruptions.</p><p>Another impediment to embracing Digital Resilience, a required new core competence, is what I call grade / position inflation in cybersecurity. Prior to 1995, I do not think there was any role in business for information technology entitled Chief Information Security Officer. There were roles in the 1970’s and 1980’s for information technology security and the role of information technology security manager was common. Steve Katz is credited as the first person appointed to a Chief Information Technology Officer role by Citi Bank in 1995 after Citi Bank suffered a series of successful cyber security attacks by Russian hackers. In the 1990’s and into the early 2000’s it was common to have an information technology security manager role reporting to a Chief Information Officer or a Chief Technology Officer. This role was often filled by a manager or director level position. As the frequency of cybersecurity attacks escalated through the 2000’s, and business impacts became larger, we begin to see the escalation of information technology security managers to higher grade levels. There was something Darwinian about the escalation of security managers to Chief Information Security Managers and further to Director and Officer roles. Today it is not uncommon to see CISO roles as Vice Presidents or Senior Vice Presidents reporting to Chief Information Officers and even to reporting lines directly to business Chief Executive Officers. Raising the position and salary levels of CISOs does not directly translate to significantly improved cybersecurity or digital resilience. Organization performance, in my experience, even for cybersecurity and digital resilience, is primary driven by organizational culture, strong governance, professional competence and employing a right role, right person approach.</p><p><strong>A Very Simple Equation</strong></p><p>My arriving at my realization for a Digital Risk and Resilience Officer role can be summarized as follows:</p><ul><li>Chief Risk Officer is a full-time role / Head of ERM is a full-time role.</li><li>CISO / ISO role is a full-time role.</li><li>CISO/ISO attention to Digital Risk and Resilience is a part-time role.</li><li>Digital Risk and Resilience calls for full-time attention given the potential for catastrophic business disruption impacts.</li><li>Thus … Digital Risk and Resilience should be a full-time role.</li><li>While maintaining the traditional Information Security Officer / Manger role.</li></ul><p><strong>The CISO / ISO role is not dead! It is time to embrace a new and improved role focused on</strong></p><p><strong>Digital Risk and Resilience.</strong></p><p>The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day. I again encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’ future.</p><p>In my next bog post, I will expand on what this might mean to CRO/ERM + DRRO integrative business approach to Enterprise Digital Risk and Resiliency.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,90,408,'en',0,'<p>I began my last blog post by quoting from a business forum I had read:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>And I closed my last post saying that I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>Some of you who read my last post may have dismissed my comment as out of hand. Others may have read a bit deeper into my comments and your reaction, at least internally, were that Joe has no clue what he is talking about. Just maybe, one or two of you may have thought about my comments and said to yourself, he may be onto something.</p><p>I do not claim to have all the clues, but I can assure you that throughout my career I have prided myself in working hard to ensure I had at least half a clue concerning what was going on around me in my professional domain.</p><p>It is a fair bet for me to say again today that:</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my 45+ years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Let me take this one step further:</p><p><em><strong>“Never in the history of modern business operations has there been a risk domain as impactful as Information Technology. Information Technology and Digital Business Systems are the only risk domain which can bring my company’s business operations to a complete standstill in an instant!”</strong></em></p><p>This is a quote from a discussion I recently had with the head of Enterprise Risk Management for a multi-billon dollar business. My conversations during the past year with many Chief Risk Officers, Enterprise Risk Managers and Chief Information Security Officers have all led me to form my opinion that it is time to start thinking about and acting on the notion that the CISO role as we know it today is past its shelf life, and it is time to seriously embrace a Digital Risk and Resilience Officer role.</p><p><strong>The CISO role as we know it today is past its shelf life.</strong></p><p><strong>It is time to embrace the</strong></p><p><strong>Digital Risk and Resilience Officer role.</strong></p><img src=\"/sites/default/files/inline-images/Security%20by%20design%2014.png\" data-entity-uuid=\"02ed5747-2c9b-41b1-8da1-f56c4f693797\" data-entity-type=\"file\" width=\"900\" height=\"394\"><p>&nbsp;</p><p>&nbsp;The traditional CISO role is intended to address the protection of an organization’s information technology, digital business systems, and data from attacks. This is often focused on external threats and can also include controls to address internal misuse of information systems and data. This is a narrow focus given the widespread use of digital systems, data, communications and workplace technologies in use throughout businesses, their customer chains and supply chains. While cybersecurity risk management focuses on cyber threats and recovery of information technology systems, it is a focus which rarely addresses the impacts to the rest of a business organization.</p><p>Enterprises Risk Management acknowledges cybersecurity threats and risks, but sees these risks through a CISO’s eye, usually not through the eyes of your business. I have found it rare when speaking with ERM leaders and Chief Risk Officers to find a fully integrative understanding of the true extent of digital disruption impacts and the need for a business integrative approach to ensure digital resilience when recovering from such business disruptions.</p><p>Another impediment to embracing Digital Resilience, a required new core competence, is what I call grade / position inflation in cybersecurity. Prior to 1995, I do not think there was any role in business for information technology entitled Chief Information Security Officer. There were roles in the 1970’s and 1980’s for information technology security and the role of information technology security manager was common. Steve Katz is credited as the first person appointed to a Chief Information Technology Officer role by Citi Bank in 1995 after Citi Bank suffered a series of successful cyber security attacks by Russian hackers. In the 1990’s and into the early 2000’s it was common to have an information technology security manager role reporting to a Chief Information Officer or a Chief Technology Officer. This role was often filled by a manager or director level position. As the frequency of cybersecurity attacks escalated through the 2000’s, and business impacts became larger, we begin to see the escalation of information technology security managers to higher grade levels. There was something Darwinian about the escalation of security managers to Chief Information Security Managers and further to Director and Officer roles. Today it is not uncommon to see CISO roles as Vice Presidents or Senior Vice Presidents reporting to Chief Information Officers and even to reporting lines directly to business Chief Executive Officers. Raising the position and salary levels of CISOs does not directly translate to significantly improved cybersecurity or digital resilience. Organization performance, in my experience, even for cybersecurity and digital resilience, is primary driven by organizational culture, strong governance, professional competence and employing a right role, right person approach.</p><p><strong>A Very Simple Equation</strong></p><p>My arriving at my realization for a Digital Risk and Resilience Officer role can be summarized as follows:</p><ul><li>Chief Risk Officer is a full-time role / Head of ERM is a full-time role.</li><li>CISO / ISO role is a full-time role.</li><li>CISO/ISO attention to Digital Risk and Resilience is a part-time role.</li><li>Digital Risk and Resilience calls for full-time attention given the potential for catastrophic business disruption impacts.</li><li>Thus … Digital Risk and Resilience should be a full-time role.</li><li>While maintaining the traditional Information Security Officer / Manger role.</li></ul><p><strong>The CISO / ISO role is not dead! It is time to embrace a new and improved role focused on</strong></p><p><strong>Digital Risk and Resilience.</strong></p><p>The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day. I again encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’ future.</p><p>In my next bog post, I will expand on what this might mean to CRO/ERM + DRRO integrative business approach to Enterprise Digital Risk and Resiliency.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,90,490,'en',0,'<p>I began my last blog post by quoting from a business forum I had read:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>And I closed my last post saying that I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>Some of you who read my last post may have dismissed my comment as out of hand. Others may have read a bit deeper into my comments and your reaction, at least internally, were that Joe has no clue what he is talking about. Just maybe, one or two of you may have thought about my comments and said to yourself, he may be onto something.</p><p>I do not claim to have all the clues, but I can assure you that throughout my career I have prided myself in working hard to ensure I had at least half a clue concerning what was going on around me in my professional domain.</p><p>It is a fair bet for me to say again today that:</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my 45+ years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Let me take this one step further:</p><p><em><strong>“Never in the history of modern business operations has there been a risk domain as impactful as Information Technology. Information Technology and Digital Business Systems are the only risk domain which can bring my company’s business operations to a complete standstill in an instant!”</strong></em></p><p>This is a quote from a discussion I recently had with the head of Enterprise Risk Management for a multi-billon dollar business. My conversations during the past year with many Chief Risk Officers, Enterprise Risk Managers and Chief Information Security Officers have all led me to form my opinion that it is time to start thinking about and acting on the notion that the CISO role as we know it today is past its shelf life, and it is time to seriously embrace a Digital Risk and Resilience Officer role.</p><p><strong>The CISO role as we know it today is past its shelf life.</strong></p><p><strong>It is time to embrace the</strong></p><p><strong>Digital Risk and Resilience Officer role.</strong></p><img src=\"/sites/default/files/inline-images/Security%20by%20design%2014.png\" data-entity-uuid=\"02ed5747-2c9b-41b1-8da1-f56c4f693797\" data-entity-type=\"file\" width=\"900\" height=\"394\"><p>&nbsp;</p><p>&nbsp;The traditional CISO role is intended to address the protection of an organization’s information technology, digital business systems, and data from attacks. This is often focused on external threats and can also include controls to address internal misuse of information systems and data. This is a narrow focus given the widespread use of digital systems, data, communications and workplace technologies in use throughout businesses, their customer chains and supply chains. While cybersecurity risk management focuses on cyber threats and recovery of information technology systems, it is a focus which rarely addresses the impacts to the rest of a business organization.</p><p>Enterprises Risk Management acknowledges cybersecurity threats and risks, but sees these risks through a CISO’s eye, usually not through the eyes of your business. I have found it rare when speaking with ERM leaders and Chief Risk Officers to find a fully integrative understanding of the true extent of digital disruption impacts and the need for a business integrative approach to ensure digital resilience when recovering from such business disruptions.</p><p>Another impediment to embracing Digital Resilience, a required new core competence, is what I call grade / position inflation in cybersecurity. Prior to 1995, I do not think there was any role in business for information technology entitled Chief Information Security Officer. There were roles in the 1970’s and 1980’s for information technology security and the role of information technology security manager was common. Steve Katz is credited as the first person appointed to a Chief Information Technology Officer role by Citi Bank in 1995 after Citi Bank suffered a series of successful cyber security attacks by Russian hackers. In the 1990’s and into the early 2000’s it was common to have an information technology security manager role reporting to a Chief Information Officer or a Chief Technology Officer. This role was often filled by a manager or director level position. As the frequency of cybersecurity attacks escalated through the 2000’s, and business impacts became larger, we begin to see the escalation of information technology security managers to higher grade levels. There was something Darwinian about the escalation of security managers to Chief Information Security Managers and further to Director and Officer roles. Today it is not uncommon to see CISO roles as Vice Presidents or Senior Vice Presidents reporting to Chief Information Officers and even to reporting lines directly to business Chief Executive Officers. Raising the position and salary levels of CISOs does not directly translate to significantly improved cybersecurity or digital resilience. Organization performance, in my experience, even for cybersecurity and digital resilience, is primary driven by organizational culture, strong governance, professional competence and employing a right role, right person approach.</p><p><strong>A Very Simple Equation</strong></p><p>My arriving at my realization for a Digital Risk and Resilience Officer role can be summarized as follows:</p><ul><li>Chief Risk Officer is a full-time role / Head of ERM is a full-time role.</li><li>CISO / ISO role is a full-time role.</li><li>CISO/ISO attention to Digital Risk and Resilience is a part-time role.</li><li>Digital Risk and Resilience calls for full-time attention given the potential for catastrophic business disruption impacts.</li><li>Thus … Digital Risk and Resilience should be a full-time role.</li><li>While maintaining the traditional Information Security Officer / Manger role.</li></ul><p><strong>The CISO / ISO role is not dead! It is time to embrace a new and improved role focused on</strong></p><p><strong>Digital Risk and Resilience.</strong></p><p>The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day. I again encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’ future.</p><p>In my next bog post, I will expand on what this might mean to CRO/ERM + DRRO integrative business approach to Enterprise Digital Risk and Resiliency.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,90,570,'en',0,'<p>I began my last blog post by quoting from a business forum I had read:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>And I closed my last post saying that I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>Some of you who read my last post may have dismissed my comment as out of hand. Others may have read a bit deeper into my comments and your reaction, at least internally, were that Joe has no clue what he is talking about. Just maybe, one or two of you may have thought about my comments and said to yourself, he may be onto something.</p><p>I do not claim to have all the clues, but I can assure you that throughout my career I have prided myself in working hard to ensure I had at least half a clue concerning what was going on around me in my professional domain.</p><p>It is a fair bet for me to say again today that:</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my 45+ years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Let me take this one step further:</p><p><em><strong>“Never in the history of modern business operations has there been a risk domain as impactful as Information Technology. Information Technology and Digital Business Systems are the only risk domain which can bring my company’s business operations to a complete standstill in an instant!”</strong></em></p><p>This is a quote from a discussion I recently had with the head of Enterprise Risk Management for a multi-billon dollar business. My conversations during the past year with many Chief Risk Officers, Enterprise Risk Managers and Chief Information Security Officers have all led me to form my opinion that it is time to start thinking about and acting on the notion that the CISO role as we know it today is past its shelf life, and it is time to seriously embrace a Digital Risk and Resilience Officer role.</p><p><strong>The CISO role as we know it today is past its shelf life.</strong></p><p><strong>It is time to embrace the</strong></p><p><strong>Digital Risk and Resilience Officer role.</strong></p><img src=\"/sites/default/files/inline-images/Security%20by%20design%2014.png\" data-entity-uuid=\"02ed5747-2c9b-41b1-8da1-f56c4f693797\" data-entity-type=\"file\" width=\"900\" height=\"394\"><p>&nbsp;</p><p>&nbsp;The traditional CISO role is intended to address the protection of an organization’s information technology, digital business systems, and data from attacks. This is often focused on external threats and can also include controls to address internal misuse of information systems and data. This is a narrow focus given the widespread use of digital systems, data, communications and workplace technologies in use throughout businesses, their customer chains and supply chains. While cybersecurity risk management focuses on cyber threats and recovery of information technology systems, it is a focus which rarely addresses the impacts to the rest of a business organization.</p><p>Enterprises Risk Management acknowledges cybersecurity threats and risks, but sees these risks through a CISO’s eye, usually not through the eyes of your business. I have found it rare when speaking with ERM leaders and Chief Risk Officers to find a fully integrative understanding of the true extent of digital disruption impacts and the need for a business integrative approach to ensure digital resilience when recovering from such business disruptions.</p><p>Another impediment to embracing Digital Resilience, a required new core competence, is what I call grade / position inflation in cybersecurity. Prior to 1995, I do not think there was any role in business for information technology entitled Chief Information Security Officer. There were roles in the 1970’s and 1980’s for information technology security and the role of information technology security manager was common. Steve Katz is credited as the first person appointed to a Chief Information Technology Officer role by Citi Bank in 1995 after Citi Bank suffered a series of successful cyber security attacks by Russian hackers. In the 1990’s and into the early 2000’s it was common to have an information technology security manager role reporting to a Chief Information Officer or a Chief Technology Officer. This role was often filled by a manager or director level position. As the frequency of cybersecurity attacks escalated through the 2000’s, and business impacts became larger, we begin to see the escalation of information technology security managers to higher grade levels. There was something Darwinian about the escalation of security managers to Chief Information Security Managers and further to Director and Officer roles. Today it is not uncommon to see CISO roles as Vice Presidents or Senior Vice Presidents reporting to Chief Information Officers and even to reporting lines directly to business Chief Executive Officers. Raising the position and salary levels of CISOs does not directly translate to significantly improved cybersecurity or digital resilience. Organization performance, in my experience, even for cybersecurity and digital resilience, is primary driven by organizational culture, strong governance, professional competence and employing a right role, right person approach.</p><p><strong>A Very Simple Equation</strong></p><p>My arriving at my realization for a Digital Risk and Resilience Officer role can be summarized as follows:</p><ul><li>Chief Risk Officer is a full-time role / Head of ERM is a full-time role.</li><li>CISO / ISO role is a full-time role.</li><li>CISO/ISO attention to Digital Risk and Resilience is a part-time role.</li><li>Digital Risk and Resilience calls for full-time attention given the potential for catastrophic business disruption impacts.</li><li>Thus … Digital Risk and Resilience should be a full-time role.</li><li>While maintaining the traditional Information Security Officer / Manger role.</li></ul><p><strong>The CISO / ISO role is not dead! It is time to embrace a new and improved role focused on</strong></p><p><strong>Digital Risk and Resilience.</strong></p><p>The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day. I again encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’ future.</p><p>In my next bog post, I will expand on what this might mean to CRO/ERM + DRRO integrative business approach to Enterprise Digital Risk and Resiliency.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,90,645,'en',0,'<p>I began my last blog post by quoting from a business forum I had read:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>And I closed my last post saying that I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>Some of you who read my last post may have dismissed my comment as out of hand. Others may have read a bit deeper into my comments and your reaction, at least internally, were that Joe has no clue what he is talking about. Just maybe, one or two of you may have thought about my comments and said to yourself, he may be onto something.</p><p>I do not claim to have all the clues, but I can assure you that throughout my career I have prided myself in working hard to ensure I had at least half a clue concerning what was going on around me in my professional domain.</p><p>It is a fair bet for me to say again today that:</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my 45+ years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Let me take this one step further:</p><p><em><strong>“Never in the history of modern business operations has there been a risk domain as impactful as Information Technology. Information Technology and Digital Business Systems are the only risk domain which can bring my company’s business operations to a complete standstill in an instant!”</strong></em></p><p>This is a quote from a discussion I recently had with the head of Enterprise Risk Management for a multi-billon dollar business. My conversations during the past year with many Chief Risk Officers, Enterprise Risk Managers and Chief Information Security Officers have all led me to form my opinion that it is time to start thinking about and acting on the notion that the CISO role as we know it today is past its shelf life, and it is time to seriously embrace a Digital Risk and Resilience Officer role.</p><p><strong>The CISO role as we know it today is past its shelf life.</strong></p><p><strong>It is time to embrace the</strong></p><p><strong>Digital Risk and Resilience Officer role.</strong></p><img src=\"/sites/default/files/inline-images/Security%20by%20design%2014.png\" data-entity-uuid=\"02ed5747-2c9b-41b1-8da1-f56c4f693797\" data-entity-type=\"file\" width=\"900\" height=\"394\"><p>&nbsp;</p><p>&nbsp;The traditional CISO role is intended to address the protection of an organization’s information technology, digital business systems, and data from attacks. This is often focused on external threats and can also include controls to address internal misuse of information systems and data. This is a narrow focus given the widespread use of digital systems, data, communications and workplace technologies in use throughout businesses, their customer chains and supply chains. While cybersecurity risk management focuses on cyber threats and recovery of information technology systems, it is a focus which rarely addresses the impacts to the rest of a business organization.</p><p>Enterprises Risk Management acknowledges cybersecurity threats and risks, but sees these risks through a CISO’s eye, usually not through the eyes of your business. I have found it rare when speaking with ERM leaders and Chief Risk Officers to find a fully integrative understanding of the true extent of digital disruption impacts and the need for a business integrative approach to ensure digital resilience when recovering from such business disruptions.</p><p>Another impediment to embracing Digital Resilience, a required new core competence, is what I call grade / position inflation in cybersecurity. Prior to 1995, I do not think there was any role in business for information technology entitled Chief Information Security Officer. There were roles in the 1970’s and 1980’s for information technology security and the role of information technology security manager was common. Steve Katz is credited as the first person appointed to a Chief Information Technology Officer role by Citi Bank in 1995 after Citi Bank suffered a series of successful cyber security attacks by Russian hackers. In the 1990’s and into the early 2000’s it was common to have an information technology security manager role reporting to a Chief Information Officer or a Chief Technology Officer. This role was often filled by a manager or director level position. As the frequency of cybersecurity attacks escalated through the 2000’s, and business impacts became larger, we begin to see the escalation of information technology security managers to higher grade levels. There was something Darwinian about the escalation of security managers to Chief Information Security Managers and further to Director and Officer roles. Today it is not uncommon to see CISO roles as Vice Presidents or Senior Vice Presidents reporting to Chief Information Officers and even to reporting lines directly to business Chief Executive Officers. Raising the position and salary levels of CISOs does not directly translate to significantly improved cybersecurity or digital resilience. Organization performance, in my experience, even for cybersecurity and digital resilience, is primary driven by organizational culture, strong governance, professional competence and employing a right role, right person approach.</p><p><strong>A Very Simple Equation</strong></p><p>My arriving at my realization for a Digital Risk and Resilience Officer role can be summarized as follows:</p><ul><li>Chief Risk Officer is a full-time role / Head of ERM is a full-time role.</li><li>CISO / ISO role is a full-time role.</li><li>CISO/ISO attention to Digital Risk and Resilience is a part-time role.</li><li>Digital Risk and Resilience calls for full-time attention given the potential for catastrophic business disruption impacts.</li><li>Thus … Digital Risk and Resilience should be a full-time role.</li><li>While maintaining the traditional Information Security Officer / Manger role.</li></ul><p><strong>The CISO / ISO role is not dead! It is time to embrace a new and improved role focused on</strong></p><p><strong>Digital Risk and Resilience.</strong></p><p>The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day. I again encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’ future.</p><p>In my next bog post, I will expand on what this might mean to CRO/ERM + DRRO integrative business approach to Enterprise Digital Risk and Resiliency.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,90,752,'en',0,'<p>I began my last blog post by quoting from a business forum I had read:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>And I closed my last post saying that I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>Some of you who read my last post may have dismissed my comment as out of hand. Others may have read a bit deeper into my comments and your reaction, at least internally, were that Joe has no clue what he is talking about. Just maybe, one or two of you may have thought about my comments and said to yourself, he may be onto something.</p><p>I do not claim to have all the clues, but I can assure you that throughout my career I have prided myself in working hard to ensure I had at least half a clue concerning what was going on around me in my professional domain.</p><p>It is a fair bet for me to say again today that:</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my 45+ years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Let me take this one step further:</p><p><em><strong>“Never in the history of modern business operations has there been a risk domain as impactful as Information Technology. Information Technology and Digital Business Systems are the only risk domain which can bring my company’s business operations to a complete standstill in an instant!”</strong></em></p><p>This is a quote from a discussion I recently had with the head of Enterprise Risk Management for a multi-billon dollar business. My conversations during the past year with many Chief Risk Officers, Enterprise Risk Managers and Chief Information Security Officers have all led me to form my opinion that it is time to start thinking about and acting on the notion that the CISO role as we know it today is past its shelf life, and it is time to seriously embrace a Digital Risk and Resilience Officer role.</p><p><strong>The CISO role as we know it today is past its shelf life.</strong></p><p><strong>It is time to embrace the</strong></p><p><strong>Digital Risk and Resilience Officer role.</strong></p><img src=\"/sites/default/files/inline-images/Security%20by%20design%2014.png\" data-entity-uuid=\"02ed5747-2c9b-41b1-8da1-f56c4f693797\" data-entity-type=\"file\" width=\"900\" height=\"394\" alt=\"Digital Resilience vs Cybersecurity\"><p>&nbsp;</p><p>&nbsp;The traditional CISO role is intended to address the protection of an organization’s information technology, digital business systems, and data from attacks. This is often focused on external threats and can also include controls to address internal misuse of information systems and data. This is a narrow focus given the widespread use of digital systems, data, communications and workplace technologies in use throughout businesses, their customer chains and supply chains. While cybersecurity risk management focuses on cyber threats and recovery of information technology systems, it is a focus which rarely addresses the impacts to the rest of a business organization.</p><p>Enterprises Risk Management acknowledges cybersecurity threats and risks, but sees these risks through a CISO’s eye, usually not through the eyes of your business. I have found it rare when speaking with ERM leaders and Chief Risk Officers to find a fully integrative understanding of the true extent of digital disruption impacts and the need for a business integrative approach to ensure digital resilience when recovering from such business disruptions.</p><p>Another impediment to embracing Digital Resilience, a required new core competence, is what I call grade / position inflation in cybersecurity. Prior to 1995, I do not think there was any role in business for information technology entitled Chief Information Security Officer. There were roles in the 1970’s and 1980’s for information technology security and the role of information technology security manager was common. Steve Katz is credited as the first person appointed to a Chief Information Technology Officer role by Citi Bank in 1995 after Citi Bank suffered a series of successful cyber security attacks by Russian hackers. In the 1990’s and into the early 2000’s it was common to have an information technology security manager role reporting to a Chief Information Officer or a Chief Technology Officer. This role was often filled by a manager or director level position. As the frequency of cybersecurity attacks escalated through the 2000’s, and business impacts became larger, we begin to see the escalation of information technology security managers to higher grade levels. There was something Darwinian about the escalation of security managers to Chief Information Security Managers and further to Director and Officer roles. Today it is not uncommon to see CISO roles as Vice Presidents or Senior Vice Presidents reporting to Chief Information Officers and even to reporting lines directly to business Chief Executive Officers. Raising the position and salary levels of CISOs does not directly translate to significantly improved cybersecurity or digital resilience. Organization performance, in my experience, even for cybersecurity and digital resilience, is primary driven by organizational culture, strong governance, professional competence and employing a right role, right person approach.</p><p><strong>A Very Simple Equation</strong></p><p>My arriving at my realization for a Digital Risk and Resilience Officer role can be summarized as follows:</p><ul><li>Chief Risk Officer is a full-time role / Head of ERM is a full-time role.</li><li>CISO / ISO role is a full-time role.</li><li>CISO/ISO attention to Digital Risk and Resilience is a part-time role.</li><li>Digital Risk and Resilience calls for full-time attention given the potential for catastrophic business disruption impacts.</li><li>Thus … Digital Risk and Resilience should be a full-time role.</li><li>While maintaining the traditional Information Security Officer / Manger role.</li></ul><p><strong>The CISO / ISO role is not dead! It is time to embrace a new and improved role focused on</strong></p><p><strong>Digital Risk and Resilience.</strong></p><p>The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day. I again encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’ future.</p><p>In my next bog post, I will expand on what this might mean to CRO/ERM + DRRO integrative business approach to Enterprise Digital Risk and Resiliency.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('blog',0,90,821,'en',0,'<p>I began my last blog post by quoting from a business forum I had read:</p><p><em><strong>“To put it bluntly, the CISO role is dead.”</strong></em></p><p>And I closed my last post saying that I would encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’s future.</p><p>Some of you who read my last post may have dismissed my comment as out of hand. Others may have read a bit deeper into my comments and your reaction, at least internally, were that Joe has no clue what he is talking about. Just maybe, one or two of you may have thought about my comments and said to yourself, he may be onto something.</p><p>I do not claim to have all the clues, but I can assure you that throughout my career I have prided myself in working hard to ensure I had at least half a clue concerning what was going on around me in my professional domain.</p><p>It is a fair bet for me to say again today that:</p><p><em><strong>The threats and risks our businesses face today are at the highest level of systemic digital risk I have experienced in my 45+ years in the Information Technology industry.</strong></em></p><p><em><strong>&amp;</strong></em></p><p><em><strong>Information Technology organizations no longer control all the information technology devices and systems in use in their organizations today.</strong></em></p><p>Let me take this one step further:</p><p><em><strong>“Never in the history of modern business operations has there been a risk domain as impactful as Information Technology. Information Technology and Digital Business Systems are the only risk domain which can bring my company’s business operations to a complete standstill in an instant!”</strong></em></p><p>This is a quote from a discussion I recently had with the head of Enterprise Risk Management for a multi-billon dollar business. My conversations during the past year with many Chief Risk Officers, Enterprise Risk Managers and Chief Information Security Officers have all led me to form my opinion that it is time to start thinking about and acting on the notion that the CISO role as we know it today is past its shelf life, and it is time to seriously embrace a Digital Risk and Resilience Officer role.</p><p><strong>The CISO role as we know it today is past its shelf life.</strong></p><p><strong>It is time to embrace the</strong></p><p><strong>Digital Risk and Resilience Officer role.</strong></p><img src=\"/sites/default/files/inline-images/Security%20by%20design%2014.png\" data-entity-uuid=\"02ed5747-2c9b-41b1-8da1-f56c4f693797\" data-entity-type=\"file\" alt=\"Digital Resilience vs Cybersecurity\" width=\"900\" height=\"394\"><p>&nbsp;</p><p>&nbsp;The traditional CISO role is intended to address the protection of an organization’s information technology, digital business systems, and data from attacks. This is often focused on external threats and can also include controls to address internal misuse of information systems and data. This is a narrow focus given the widespread use of digital systems, data, communications and workplace technologies in use throughout businesses, their customer chains and supply chains. While cybersecurity risk management focuses on cyber threats and recovery of information technology systems, it is a focus which rarely addresses the impacts to the rest of a business organization.</p><p>Enterprises Risk Management acknowledges cybersecurity threats and risks, but sees these risks through a CISO’s eye, usually not through the eyes of your business. I have found it rare when speaking with ERM leaders and Chief Risk Officers to find a fully integrative understanding of the true extent of digital disruption impacts and the need for a business integrative approach to ensure digital resilience when recovering from such business disruptions.</p><p>Another impediment to embracing Digital Resilience, a required new core competence, is what I call grade / position inflation in cybersecurity. Prior to 1995, I do not think there was any role in business for information technology entitled Chief Information Security Officer. There were roles in the 1970’s and 1980’s for information technology security and the role of information technology security manager was common. Steve Katz is credited as the first person appointed to a Chief Information Technology Officer role by Citi Bank in 1995 after Citi Bank suffered a series of successful cyber security attacks by Russian hackers. In the 1990’s and into the early 2000’s it was common to have an information technology security manager role reporting to a Chief Information Officer or a Chief Technology Officer. This role was often filled by a manager or director level position. As the frequency of cybersecurity attacks escalated through the 2000’s, and business impacts became larger, we begin to see the escalation of information technology security managers to higher grade levels. There was something Darwinian about the escalation of security managers to Chief Information Security Managers and further to Director and Officer roles. Today it is not uncommon to see CISO roles as Vice Presidents or Senior Vice Presidents reporting to Chief Information Officers and even to reporting lines directly to business Chief Executive Officers. Raising the position and salary levels of CISOs does not directly translate to significantly improved cybersecurity or digital resilience. Organization performance, in my experience, even for cybersecurity and digital resilience, is primary driven by organizational culture, strong governance, professional competence and employing a right role, right person approach.</p><p><strong>A Very Simple Equation</strong></p><p>My arriving at my realization for a Digital Risk and Resilience Officer role can be summarized as follows:</p><ul><li>Chief Risk Officer is a full-time role / Head of ERM is a full-time role.</li><li>CISO / ISO role is a full-time role.</li><li>CISO/ISO attention to Digital Risk and Resilience is a part-time role.</li><li>Digital Risk and Resilience calls for full-time attention given the potential for catastrophic business disruption impacts.</li><li>Thus … Digital Risk and Resilience should be a full-time role.</li><li>While maintaining the traditional Information Security Officer / Manger role.</li></ul><p><strong>The CISO / ISO role is not dead! It is time to embrace a new and improved role focused on</strong></p><p><strong>Digital Risk and Resilience.</strong></p><p>The Digital Risk and Resilience Officer role should represent a significant evolution in purpose, responsibility and vision for dealing with the threats and systemic risks our businesses now face every day. I again encourage you all to think about the potential of a Digital Risk and Resilience Officer role and focus as an evolution needed in Information Security which will be more encompassing and vital to the success of your business’ future.</p><p>In my next bog post, I will expand on what this might mean to CRO/ERM + DRRO integrative business approach to Enterprise Digital Risk and Resiliency.</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Joseph F. Norton</strong> is a Risk, Security, and Crisis Management professional.</p><p>He is a founding member and Qualified Technology Executive of the Digital Directors Network, Special Advisor for Risk and Cybersecurity to DivIHN, Chair of the Advisory Board with Next Era Transformation Group, and Chief Security Officer with APF Technologies.</p><p>He has served as Chief Security Officer, SVP at Atos, Chief Technology Officer and Head of Operations, SVP at Philips, Chief Technology Officer, SVP at Novartis, Executive-in-Residence with McKinsey &amp; Company, and Chief Technology Officer at McDonald’s. He has also held professional roles during his career with JPMorgan Bank, Oracle, Sybase and Grumman Aerospace Corporation, and the United States Navy.</p>','','full_html'),('webinar',0,91,224,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,225,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,226,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,227,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,257,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,409,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,434,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,435,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,438,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,491,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,492,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,513,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,571,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,646,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,731,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,748,'en',0,'<h4 class=\"WebinarHeading\"><strong>About Webinar</strong></h4><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,858,'en',0,'<h4 class=\"WebinarHeading\"><strong>About Webinar</strong></h4><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,859,'en',0,'<h4 class=\"WebinarHeading\"><strong>About Webinar</strong></h4><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,91,947,'en',0,'<h4 class=\"WebinarHeading\"><strong>About Webinar</strong></h4><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('unique_template_pages',0,93,273,'en',0,'<p>Opportunities in the areas of Information Technology,&nbsp;<br>Life Sciences, Engineering, and more with our Enterprise,&nbsp;<br>mid-market and government clients.&nbsp;</p><p><a class=\"primary-btn contactUsBtn\" href=\"/careers/consultant-careers/open-jobs\" aria-label=\"View our Open Jobs\" tabindex=\"0\" role=\"link\" data-content=\"View our Open Jobs\" target=\"_blank\">View our Open Jobs</a></p>','','full_html'),('unique_template_pages',0,95,275,'en',0,'<p>At DivIHN, we believe meaningful work empowers,<br>meaningful lives. Join our passionate team of individuals<br>devoted to transforming lives and making a positive impact.</p><p><a class=\"primary-btn contactUsBtn\" href=\"/careers/internal-careers/open-positions\" aria-label=\"View our Open Positions\" tabindex=\"0\" role=\"link\" data-content=\"View our Open Positions\" target=\"_blank\">View our Open Positions</a></p>','','full_html'),('client_success',0,96,276,'en',0,'<p><strong>Our client, a leading independent producer of high-quality, specialty hydrocarbon products, agreed to sell a 50,000 barrel per day refinery, associated delivery terminals, and related assets to an unaffiliated energy company for $435 million.</strong></p>','','basic_html'),('client_success',0,96,291,'en',0,'<p>Our client, a leading independent producer of high-quality, specialty hydrocarbon products, agreed to sell a 50,000 barrel per day refinery, associated delivery terminals, and related assets to an unaffiliated energy company for $435 million.</p>','','basic_html'),('client_success',0,96,301,'en',0,'<p>Our client, a leading independent producer of high-quality, specialty hydrocarbon products, agreed to sell a 50,000 barrel per day refinery, associated delivery terminals, and related assets to an unaffiliated energy company for $435 million.</p>','','basic_html'),('client_success',0,96,336,'en',0,'<p>Our client, a leading independent producer of high-quality, specialty hydrocarbon products, agreed to sell a 50,000 barrel per day refinery, associated delivery terminals, and related assets to an unaffiliated energy company for $435 million.</p>','','basic_html'),('client_success',0,96,350,'en',0,'<p>Our client, a leading independent producer of high-quality, specialty hydrocarbon products, agreed to sell a 50,000 barrel per day refinery, associated delivery terminals, and related assets to an unaffiliated energy company for $435 million.</p>','','basic_html'),('client_success',0,96,363,'en',0,'<p>Our client, a leading independent producer of high-quality, specialty hydrocarbon products, agreed to sell a 50,000 barrel per day refinery, associated delivery terminals, and related assets to an unaffiliated energy company for $435 million.</p>','','basic_html'),('client_success',0,96,384,'en',0,'<p>Our client, a leading independent producer of high-quality, specialty hydrocarbon products, agreed to sell a 50,000 barrel per day refinery, associated delivery terminals, and related assets to an unaffiliated energy company for $435 million.</p>','','basic_html'),('client_success',0,96,573,'en',0,'<p>Our client, a leading independent producer of high-quality, specialty hydrocarbon products, agreed to sell a 50,000 barrel per day refinery, associated delivery terminals, and related assets to an unaffiliated energy company for $435 million.</p>','','basic_html'),('client_success',0,97,277,'en',0,'<p>DivIHN\'s solution fostered innovation in addition to objectively quantifying project priorities. This helped the CISO improve Service Levels while managing expectations of user/business communities.</p>','','basic_html'),('client_success',0,97,278,'en',0,'<p><strong>DivIHN\'s solution fostered innovation in addition to objectively quantifying project priorities. This helped the CISO improve Service Levels while managing expectations of user/business communities.</strong></p>','','basic_html'),('client_success',0,97,292,'en',0,'<p>DivIHN\'s solution fostered innovation in addition to objectively quantifying project priorities. This helped the CISO improve Service Levels while managing expectations of user/business communities.</p>','','basic_html'),('client_success',0,97,330,'en',0,'<p>DivIHN\'s solution fostered innovation in addition to objectively quantifying project priorities. 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class=\"short_description\"><span>In an increasingly data-driven future, we guide you on the path to data enlightenment, leveraging Advanced Analytics,</span> <span>AI, and Machine Learning.</span></p>','','full_html'),('specialization_pages',0,123,441,'en',0,'<p class=\"short_description\"><span>We develop innovative solutions to unleash the full power of the Salesforce platform for your business.</span></p>','','full_html'),('specialization_pages',0,124,442,'en',0,'<p class=\"short_description\"><span>We enhance modern workplace and collaboration</span> <span>with the Microsoft Technology Stack. Microsoft</span> <span>provides the technology, and we provide the know-how, creating unparalleled value.</span></p>','','full_html'),('specialization_pages',0,125,443,'en',0,'<p class=\"short_description\"><span>Our expertise spans Cyber-People, Cyber-Processes,Cyber-Technologies, and Cyber Governance. We help you specialize and</span> <span>excel in your business while staying secure.</span></p>','','full_html'),('specialization_pages',0,126,444,'en',0,'<p class=\"short_description\"><span>Streamline workflows, automate processes, and drive innovation effortlessly.</span> <span>With ServiceNow, transform your business for a smarter, seamless future.</span></p>','','full_html'),('blog',0,131,493,'en',0,'<p>Across industries, employees are quietly embracing generative and agentic AI to work faster and smarter, using chatbots to draft content, summarize data, and even code through “vibe coding.”</p><p>A recent KPMG study reveals that 58% of employees now use AI tools daily, often because they outperform enterprise systems. But speed without oversight creates exposure. Nearly half admit to uploading sensitive company data to unauthorized platforms. What began as a productivity boost is quickly becoming a hidden security threat operating inside the firewall.</p><p><strong>Potential Security Threats Executives Should Not Ignore</strong></p><p>Shadow AI does not attack from the outside; it emerges quietly within enterprise workflows, making it especially dangerous Below are the most pressing threats organizations face today:</p><img src=\"/sites/default/files/inline-images/Blog_Creative_image_1.png\" data-entity-uuid=\"84c0384c-25a0-4401-a498-140829c1cbe8\" data-entity-type=\"file\" width=\"879\" height=\"210\"><ol><li><p><strong>Sensitive Data Leakage and Exposure</strong></p><p><br>Employees often paste confidential information, customer details, codes, and strategy documents into public AI tools. That data may be retained by third-party systems, creating irreversible exposure with no audit trail.</p></li><li><p><strong>AI-Powered Attack Amplification</strong></p><p><br>Leaked outputs from unsanctioned AI use can be repurposed by adversaries to design far more convincing attacks. Threat actors can build tailored phishing, voice, or video impersonations and social-engineering schemes that closely mirror an organization’s tone and procedures, which increases the likelihood of successful compromise.</p></li><li><p><strong>Compliance Violations</strong></p><p>Shadow AI can breach data privacy laws such as GDPR, HIPAA, or SOC 2. Because these tools operate outside corporate compliance frameworks, they bypass consent, retention, and auditing controls.</p></li><li><p><strong>Model Manipulation and Bias Risks</strong></p><p><br>Unverified AI models can generate inaccurate, biased, or misleading outputs that influence decisions, from HR screening to financial projections. Without governance, these models can quietly distort operations.</p></li><li><p><strong>Data Exfiltration at Scale</strong></p><p><br>Shadow AI does not just leak data; it learns from it. Each prompt containing proprietary details reveals how teams work, decide, and think. Over time, these interactions can expose an organization’s logic, strategy, and IP, creating a continuous flow of insight leaving the enterprise without detection.</p><p>These risks are no longer theoretical; they represent an evolving cyber threat vector that traditional controls cannot detect or mitigate.</p></li></ol><p><strong>The Hidden Cost of Unchecked AI Use</strong></p><p>The danger of Shadow AI extends beyond cost exposure; it is a governance gap that undermines trust and accountability.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative2.png\" data-entity-uuid=\"c488f7b8-5bee-430d-a024-9f155f60479c\" data-entity-type=\"file\" width=\"423\" height=\"466\"></p><p>AI models trained on uncontrolled data can produce outputs that distort business logic or introduce regulatory violations. Worse, when AI operates outside enterprise oversight, compliance teams lose visibility into data lineage, model reliability, and decision integrity.</p><p>Unverified external servers may store a single query containing sensitive information indefinitely, thereby exposing the organization to financial, legal, and reputational risk.</p><p>In the era of accelerated AI adoption, an unmanaged AI ecosystem has become the new insider threat.</p><p><strong>Building a Governance Model for Shadow AI</strong></p><p>Employees use unapproved AI tools to solve real problems, not to bypass policy. Forward-thinking leaders can transform this behavior into a managed advantage.</p><p>Build secure AI sandboxes where teams can safely experiment using anonymized or synthetic data. Deploy AI-aware data loss prevention (DLP) and cloud access security broker (CASB) solutions to improve visibility. Classify tools as approved, restricted, or prohibited based on assessed risk.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative3.png\" data-entity-uuid=\"68534c99-42ee-4afe-be6e-08ef92c3da3d\" data-entity-type=\"file\" width=\"570\" height=\"403\"></p><p>Shift the mindset from enforcement to enablement by promoting transparency, open reporting, and continuous training. Learn from employee innovation patterns to accelerate enterprise AI adoption securely.</p><p>Shadow AI cannot be eliminated, but it can be governed. Effective governance is not restriction; it is about creating guardrails where innovation can thrive responsibly.</p><p><strong>Leadership’s Role: From Awareness to Action</strong></p><p>For today’s C-suite, Shadow AI is not just a technical challenge; it is a strategic and cultural one.</p><p>Every decision carries a digital dependency, and every digital process leaves an AI footprint. Whether an organization embraces AI or hesitates, the risks already live within its workflows.</p><p>Leaders must guide the shift from “move fast” to “move smart.”<br>That means fostering innovation while enforcing ethical, transparent, and compliant use of AI. The strongest AI-driven enterprises will be those that govern innovation well, not those that simply adopt it first.</p><p><strong>The Next Step: Enable Innovation Without the Shadow AI</strong></p><p>The rise of Shadow AI marks a pivotal moment in enterprise governance.</p><p>If left unchecked, it can undermine compliance, trust, and data integrity.<br>If governed effectively, it becomes a powerful catalyst for secure, compliant, and scalable innovation.</p><p>To help business leaders balance control with creativity, DivIHN Integration, Inc. invites you to an exclusive webinar:</p><p><strong>“Enable Innovation Without the Shadow AI”</strong></p><p>Join industry experts and technology leaders as they share actionable strategies for governing AI responsibly, protecting data, and accelerating enterprise innovation through secure, managed adoption.</p><p><strong>Date:</strong> Thursday, November 13, 2025<br><strong>Time:</strong> 11:00 AM to 12:00 PM CT</p>','','full_html'),('blog',0,131,494,'en',0,'<p>Across industries, employees are quietly embracing generative and agentic AI to work faster and smarter, using chatbots to draft content, summarize data, and even code through “vibe coding.”</p><p>A recent KPMG study reveals that 58% of employees now use AI tools daily, often because they outperform enterprise systems. But speed without oversight creates exposure. Nearly half admit to uploading sensitive company data to unauthorized platforms. What began as a productivity boost is quickly becoming a hidden security threat operating inside the firewall.</p><p><strong>Potential Security Threats Executives Should Not Ignore</strong></p><p>Shadow AI does not attack from the outside; it emerges quietly within enterprise workflows, making it especially dangerous Below are the most pressing threats organizations face today:</p><img src=\"/sites/default/files/inline-images/Blog_Creative_image_1.png\" data-entity-uuid=\"84c0384c-25a0-4401-a498-140829c1cbe8\" data-entity-type=\"file\" width=\"879\" height=\"210\"><ol><li><p><strong>Sensitive Data Leakage and Exposure</strong></p><p><br>Employees often paste confidential information, customer details, codes, and strategy documents into public AI tools. That data may be retained by third-party systems, creating irreversible exposure with no audit trail.</p></li><li><p><strong>AI-Powered Attack Amplification</strong></p><p><br>Leaked outputs from unsanctioned AI use can be repurposed by adversaries to design far more convincing attacks. Threat actors can build tailored phishing, voice, or video impersonations and social-engineering schemes that closely mirror an organization’s tone and procedures, which increases the likelihood of successful compromise.</p></li><li><p><strong>Compliance Violations</strong></p><p>Shadow AI can breach data privacy laws such as GDPR, HIPAA, or SOC 2. Because these tools operate outside corporate compliance frameworks, they bypass consent, retention, and auditing controls.</p></li><li><p><strong>Model Manipulation and Bias Risks</strong></p><p><br>Unverified AI models can generate inaccurate, biased, or misleading outputs that influence decisions, from HR screening to financial projections. Without governance, these models can quietly distort operations.</p></li><li><p><strong>Data Exfiltration at Scale</strong></p><p><br>Shadow AI does not just leak data; it learns from it. Each prompt containing proprietary details reveals how teams work, decide, and think. Over time, these interactions can expose an organization’s logic, strategy, and IP, creating a continuous flow of insight leaving the enterprise without detection.</p><p>These risks are no longer theoretical; they represent an evolving cyber threat vector that traditional controls cannot detect or mitigate.</p></li></ol><p><strong>The Hidden Cost of Unchecked AI Use</strong></p><p>The danger of Shadow AI extends beyond cost exposure; it is a governance gap that undermines trust and accountability.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative2.png\" data-entity-uuid=\"c488f7b8-5bee-430d-a024-9f155f60479c\" data-entity-type=\"file\" width=\"423\" height=\"466\"></p><p>AI models trained on uncontrolled data can produce outputs that distort business logic or introduce regulatory violations. Worse, when AI operates outside enterprise oversight, compliance teams lose visibility into data lineage, model reliability, and decision integrity.</p><p>Unverified external servers may store a single query containing sensitive information indefinitely, thereby exposing the organization to financial, legal, and reputational risk.</p><p>In the era of accelerated AI adoption, an unmanaged AI ecosystem has become the new insider threat.</p><p><strong>Building a Governance Model for Shadow AI</strong></p><p>Employees use unapproved AI tools to solve real problems, not to bypass policy. Forward-thinking leaders can transform this behavior into a managed advantage.</p><p>Build secure AI sandboxes where teams can safely experiment using anonymized or synthetic data. Deploy AI-aware data loss prevention (DLP) and cloud access security broker (CASB) solutions to improve visibility. Classify tools as approved, restricted, or prohibited based on assessed risk.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative3.png\" data-entity-uuid=\"68534c99-42ee-4afe-be6e-08ef92c3da3d\" data-entity-type=\"file\" width=\"570\" height=\"403\"></p><p>Shift the mindset from enforcement to enablement by promoting transparency, open reporting, and continuous training. Learn from employee innovation patterns to accelerate enterprise AI adoption securely.</p><p>Shadow AI cannot be eliminated, but it can be governed. Effective governance is not restriction; it is about creating guardrails where innovation can thrive responsibly.</p><p><strong>Leadership’s Role: From Awareness to Action</strong></p><p>For today’s C-suite, Shadow AI is not just a technical challenge; it is a strategic and cultural one.</p><p>Every decision carries a digital dependency, and every digital process leaves an AI footprint. Whether an organization embraces AI or hesitates, the risks already live within its workflows.</p><p>Leaders must guide the shift from “move fast” to “move smart.”<br>That means fostering innovation while enforcing ethical, transparent, and compliant use of AI. The strongest AI-driven enterprises will be those that govern innovation well, not those that simply adopt it first.</p><p><strong>The Next Step: Enable Innovation Without the Shadow AI</strong></p><p>The rise of Shadow AI marks a pivotal moment in enterprise governance.</p><p>If left unchecked, it can undermine compliance, trust, and data integrity.<br>If governed effectively, it becomes a powerful catalyst for secure, compliant, and scalable innovation.</p><p>To help business leaders balance control with creativity, DivIHN Integration, Inc. invites you to an exclusive webinar:</p><p><strong>“Enable Innovation Without the Shadow AI”</strong></p><p>Join industry experts and technology leaders as they share actionable strategies for governing AI responsibly, protecting data, and accelerating enterprise innovation through secure, managed adoption.</p><p><strong>Date:</strong> Thursday, November 13, 2025<br><strong>Time:</strong> 11:00 AM to 12:00 PM CT</p>','','full_html'),('blog',0,131,572,'en',0,'<p>Across industries, employees are quietly embracing generative and agentic AI to work faster and smarter, using chatbots to draft content, summarize data, and even code through “vibe coding.”</p><p>A recent KPMG study reveals that 58% of employees now use AI tools daily, often because they outperform enterprise systems. But speed without oversight creates exposure. Nearly half admit to uploading sensitive company data to unauthorized platforms. What began as a productivity boost is quickly becoming a hidden security threat operating inside the firewall.</p><p><strong>Potential Security Threats Executives Should Not Ignore</strong></p><p>Shadow AI does not attack from the outside; it emerges quietly within enterprise workflows, making it especially dangerous Below are the most pressing threats organizations face today:</p><img src=\"/sites/default/files/inline-images/Blog_Creative_image_1.png\" data-entity-uuid=\"84c0384c-25a0-4401-a498-140829c1cbe8\" data-entity-type=\"file\" width=\"879\" height=\"210\"><ol><li><p><strong>Sensitive Data Leakage and Exposure</strong></p><p><br>Employees often paste confidential information, customer details, codes, and strategy documents into public AI tools. That data may be retained by third-party systems, creating irreversible exposure with no audit trail.</p></li><li><p><strong>AI-Powered Attack Amplification</strong></p><p><br>Leaked outputs from unsanctioned AI use can be repurposed by adversaries to design far more convincing attacks. Threat actors can build tailored phishing, voice, or video impersonations and social-engineering schemes that closely mirror an organization’s tone and procedures, which increases the likelihood of successful compromise.</p></li><li><p><strong>Compliance Violations</strong></p><p>Shadow AI can breach data privacy laws such as GDPR, HIPAA, or SOC 2. Because these tools operate outside corporate compliance frameworks, they bypass consent, retention, and auditing controls.</p></li><li><p><strong>Model Manipulation and Bias Risks</strong></p><p><br>Unverified AI models can generate inaccurate, biased, or misleading outputs that influence decisions, from HR screening to financial projections. Without governance, these models can quietly distort operations.</p></li><li><p><strong>Data Exfiltration at Scale</strong></p><p><br>Shadow AI does not just leak data; it learns from it. Each prompt containing proprietary details reveals how teams work, decide, and think. Over time, these interactions can expose an organization’s logic, strategy, and IP, creating a continuous flow of insight leaving the enterprise without detection.</p><p>These risks are no longer theoretical; they represent an evolving cyber threat vector that traditional controls cannot detect or mitigate.</p></li></ol><p><strong>The Hidden Cost of Unchecked AI Use</strong></p><p>The danger of Shadow AI extends beyond cost exposure; it is a governance gap that undermines trust and accountability.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative2.png\" data-entity-uuid=\"c488f7b8-5bee-430d-a024-9f155f60479c\" data-entity-type=\"file\" width=\"423\" height=\"466\"></p><p>AI models trained on uncontrolled data can produce outputs that distort business logic or introduce regulatory violations. Worse, when AI operates outside enterprise oversight, compliance teams lose visibility into data lineage, model reliability, and decision integrity.</p><p>Unverified external servers may store a single query containing sensitive information indefinitely, thereby exposing the organization to financial, legal, and reputational risk.</p><p>In the era of accelerated AI adoption, an unmanaged AI ecosystem has become the new insider threat.</p><p><strong>Building a Governance Model for Shadow AI</strong></p><p>Employees use unapproved AI tools to solve real problems, not to bypass policy. Forward-thinking leaders can transform this behavior into a managed advantage.</p><p>Build secure AI sandboxes where teams can safely experiment using anonymized or synthetic data. Deploy AI-aware data loss prevention (DLP) and cloud access security broker (CASB) solutions to improve visibility. Classify tools as approved, restricted, or prohibited based on assessed risk.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative3.png\" data-entity-uuid=\"68534c99-42ee-4afe-be6e-08ef92c3da3d\" data-entity-type=\"file\" width=\"570\" height=\"403\"></p><p>Shift the mindset from enforcement to enablement by promoting transparency, open reporting, and continuous training. Learn from employee innovation patterns to accelerate enterprise AI adoption securely.</p><p>Shadow AI cannot be eliminated, but it can be governed. Effective governance is not restriction; it is about creating guardrails where innovation can thrive responsibly.</p><p><strong>Leadership’s Role: From Awareness to Action</strong></p><p>For today’s C-suite, Shadow AI is not just a technical challenge; it is a strategic and cultural one.</p><p>Every decision carries a digital dependency, and every digital process leaves an AI footprint. Whether an organization embraces AI or hesitates, the risks already live within its workflows.</p><p>Leaders must guide the shift from “move fast” to “move smart.”<br>That means fostering innovation while enforcing ethical, transparent, and compliant use of AI. The strongest AI-driven enterprises will be those that govern innovation well, not those that simply adopt it first.</p><p><strong>The Next Step: Enable Innovation Without the Shadow AI</strong></p><p>The rise of Shadow AI marks a pivotal moment in enterprise governance.</p><p>If left unchecked, it can undermine compliance, trust, and data integrity.<br>If governed effectively, it becomes a powerful catalyst for secure, compliant, and scalable innovation.</p><p>To help business leaders balance control with creativity, DivIHN Integration, Inc. invites you to an exclusive webinar:</p><p><strong>“Enable Innovation Without the Shadow AI”</strong></p><p>Join industry experts and technology leaders as they share actionable strategies for governing AI responsibly, protecting data, and accelerating enterprise innovation through secure, managed adoption.</p><p><strong>Date:</strong> Thursday, November 13, 2025<br><strong>Time:</strong> 11:00 AM to 12:00 PM CT</p>','','full_html'),('blog',0,131,647,'en',0,'<p>Across industries, employees are quietly embracing generative and agentic AI to work faster and smarter, using chatbots to draft content, summarize data, and even code through “vibe coding.”</p><p>A recent KPMG study reveals that 58% of employees now use AI tools daily, often because they outperform enterprise systems. But speed without oversight creates exposure. Nearly half admit to uploading sensitive company data to unauthorized platforms. What began as a productivity boost is quickly becoming a hidden security threat operating inside the firewall.</p><p><strong>Potential Security Threats Executives Should Not Ignore</strong></p><p>Shadow AI does not attack from the outside; it emerges quietly within enterprise workflows, making it especially dangerous Below are the most pressing threats organizations face today:</p><img src=\"/sites/default/files/inline-images/Blog_Creative_image_1.png\" data-entity-uuid=\"84c0384c-25a0-4401-a498-140829c1cbe8\" data-entity-type=\"file\" width=\"879\" height=\"210\"><ol><li><p><strong>Sensitive Data Leakage and Exposure</strong></p><p><br>Employees often paste confidential information, customer details, codes, and strategy documents into public AI tools. That data may be retained by third-party systems, creating irreversible exposure with no audit trail.</p></li><li><p><strong>AI-Powered Attack Amplification</strong></p><p><br>Leaked outputs from unsanctioned AI use can be repurposed by adversaries to design far more convincing attacks. Threat actors can build tailored phishing, voice, or video impersonations and social-engineering schemes that closely mirror an organization’s tone and procedures, which increases the likelihood of successful compromise.</p></li><li><p><strong>Compliance Violations</strong></p><p>Shadow AI can breach data privacy laws such as GDPR, HIPAA, or SOC 2. Because these tools operate outside corporate compliance frameworks, they bypass consent, retention, and auditing controls.</p></li><li><p><strong>Model Manipulation and Bias Risks</strong></p><p><br>Unverified AI models can generate inaccurate, biased, or misleading outputs that influence decisions, from HR screening to financial projections. Without governance, these models can quietly distort operations.</p></li><li><p><strong>Data Exfiltration at Scale</strong></p><p><br>Shadow AI does not just leak data; it learns from it. Each prompt containing proprietary details reveals how teams work, decide, and think. Over time, these interactions can expose an organization’s logic, strategy, and IP, creating a continuous flow of insight leaving the enterprise without detection.</p><p>These risks are no longer theoretical; they represent an evolving cyber threat vector that traditional controls cannot detect or mitigate.</p></li></ol><p><strong>The Hidden Cost of Unchecked AI Use</strong></p><p>The danger of Shadow AI extends beyond cost exposure; it is a governance gap that undermines trust and accountability.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative2.png\" data-entity-uuid=\"c488f7b8-5bee-430d-a024-9f155f60479c\" data-entity-type=\"file\" width=\"423\" height=\"466\"></p><p>AI models trained on uncontrolled data can produce outputs that distort business logic or introduce regulatory violations. Worse, when AI operates outside enterprise oversight, compliance teams lose visibility into data lineage, model reliability, and decision integrity.</p><p>Unverified external servers may store a single query containing sensitive information indefinitely, thereby exposing the organization to financial, legal, and reputational risk.</p><p>In the era of accelerated AI adoption, an unmanaged AI ecosystem has become the new insider threat.</p><p><strong>Building a Governance Model for Shadow AI</strong></p><p>Employees use unapproved AI tools to solve real problems, not to bypass policy. Forward-thinking leaders can transform this behavior into a managed advantage.</p><p>Build secure AI sandboxes where teams can safely experiment using anonymized or synthetic data. Deploy AI-aware data loss prevention (DLP) and cloud access security broker (CASB) solutions to improve visibility. Classify tools as approved, restricted, or prohibited based on assessed risk.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative3.png\" data-entity-uuid=\"68534c99-42ee-4afe-be6e-08ef92c3da3d\" data-entity-type=\"file\" width=\"570\" height=\"403\"></p><p>Shift the mindset from enforcement to enablement by promoting transparency, open reporting, and continuous training. Learn from employee innovation patterns to accelerate enterprise AI adoption securely.</p><p>Shadow AI cannot be eliminated, but it can be governed. Effective governance is not restriction; it is about creating guardrails where innovation can thrive responsibly.</p><p><strong>Leadership’s Role: From Awareness to Action</strong></p><p>For today’s C-suite, Shadow AI is not just a technical challenge; it is a strategic and cultural one.</p><p>Every decision carries a digital dependency, and every digital process leaves an AI footprint. Whether an organization embraces AI or hesitates, the risks already live within its workflows.</p><p>Leaders must guide the shift from “move fast” to “move smart.”<br>That means fostering innovation while enforcing ethical, transparent, and compliant use of AI. The strongest AI-driven enterprises will be those that govern innovation well, not those that simply adopt it first.</p><p><strong>The Next Step: Enable Innovation Without the Shadow AI</strong></p><p>The rise of Shadow AI marks a pivotal moment in enterprise governance.</p><p>If left unchecked, it can undermine compliance, trust, and data integrity.<br>If governed effectively, it becomes a powerful catalyst for secure, compliant, and scalable innovation.</p><p>To help business leaders balance control with creativity, DivIHN Integration, Inc. invites you to an exclusive webinar:</p><p><strong>“Enable Innovation Without the Shadow AI”</strong></p><p>Join industry experts and technology leaders as they share actionable strategies for governing AI responsibly, protecting data, and accelerating enterprise innovation through secure, managed adoption.</p><p><strong>Date:</strong> Thursday, November 13, 2025<br><strong>Time:</strong> 11:00 AM to 12:00 PM CT</p>','','full_html'),('blog',0,131,753,'en',0,'<p>Across industries, employees are quietly embracing generative and agentic AI to work faster and smarter, using chatbots to draft content, summarize data, and even code through “vibe coding.”</p><p>A recent KPMG study reveals that 58% of employees now use AI tools daily, often because they outperform enterprise systems. But speed without oversight creates exposure. Nearly half admit to uploading sensitive company data to unauthorized platforms. What began as a productivity boost is quickly becoming a hidden security threat operating inside the firewall.</p><p><strong>Potential Security Threats Executives Should Not Ignore</strong></p><p>Shadow AI does not attack from the outside; it emerges quietly within enterprise workflows, making it especially dangerous Below are the most pressing threats organizations face today:</p><img src=\"/sites/default/files/inline-images/Blog_Creative_image_1.png\" data-entity-uuid=\"84c0384c-25a0-4401-a498-140829c1cbe8\" data-entity-type=\"file\" width=\"879\" height=\"210\" alt=\"Top 5 Risks executives should now\"><ol><li><p><strong>Sensitive Data Leakage and Exposure</strong></p><p><br>Employees often paste confidential information, customer details, codes, and strategy documents into public AI tools. That data may be retained by third-party systems, creating irreversible exposure with no audit trail.</p></li><li><p><strong>AI-Powered Attack Amplification</strong></p><p><br>Leaked outputs from unsanctioned AI use can be repurposed by adversaries to design far more convincing attacks. Threat actors can build tailored phishing, voice, or video impersonations and social-engineering schemes that closely mirror an organization’s tone and procedures, which increases the likelihood of successful compromise.</p></li><li><p><strong>Compliance Violations</strong></p><p>Shadow AI can breach data privacy laws such as GDPR, HIPAA, or SOC 2. Because these tools operate outside corporate compliance frameworks, they bypass consent, retention, and auditing controls.</p></li><li><p><strong>Model Manipulation and Bias Risks</strong></p><p><br>Unverified AI models can generate inaccurate, biased, or misleading outputs that influence decisions, from HR screening to financial projections. Without governance, these models can quietly distort operations.</p></li><li><p><strong>Data Exfiltration at Scale</strong></p><p><br>Shadow AI does not just leak data; it learns from it. Each prompt containing proprietary details reveals how teams work, decide, and think. Over time, these interactions can expose an organization’s logic, strategy, and IP, creating a continuous flow of insight leaving the enterprise without detection.</p><p>These risks are no longer theoretical; they represent an evolving cyber threat vector that traditional controls cannot detect or mitigate.</p></li></ol><p><strong>The Hidden Cost of Unchecked AI Use</strong></p><p>The danger of Shadow AI extends beyond cost exposure; it is a governance gap that undermines trust and accountability.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative2.png\" data-entity-uuid=\"c488f7b8-5bee-430d-a024-9f155f60479c\" data-entity-type=\"file\" width=\"423\" height=\"466\" alt=\"vertice 2025 source \"></p><p>AI models trained on uncontrolled data can produce outputs that distort business logic or introduce regulatory violations. Worse, when AI operates outside enterprise oversight, compliance teams lose visibility into data lineage, model reliability, and decision integrity.</p><p>Unverified external servers may store a single query containing sensitive information indefinitely, thereby exposing the organization to financial, legal, and reputational risk.</p><p>In the era of accelerated AI adoption, an unmanaged AI ecosystem has become the new insider threat.</p><p><strong>Building a Governance Model for Shadow AI</strong></p><p>Employees use unapproved AI tools to solve real problems, not to bypass policy. Forward-thinking leaders can transform this behavior into a managed advantage.</p><p>Build secure AI sandboxes where teams can safely experiment using anonymized or synthetic data. Deploy AI-aware data loss prevention (DLP) and cloud access security broker (CASB) solutions to improve visibility. Classify tools as approved, restricted, or prohibited based on assessed risk.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative3.png\" data-entity-uuid=\"68534c99-42ee-4afe-be6e-08ef92c3da3d\" data-entity-type=\"file\" width=\"570\" height=\"403\" alt=\"Vertice 2025 Source\"></p><p>Shift the mindset from enforcement to enablement by promoting transparency, open reporting, and continuous training. Learn from employee innovation patterns to accelerate enterprise AI adoption securely.</p><p>Shadow AI cannot be eliminated, but it can be governed. Effective governance is not restriction; it is about creating guardrails where innovation can thrive responsibly.</p><p><strong>Leadership’s Role: From Awareness to Action</strong></p><p>For today’s C-suite, Shadow AI is not just a technical challenge; it is a strategic and cultural one.</p><p>Every decision carries a digital dependency, and every digital process leaves an AI footprint. Whether an organization embraces AI or hesitates, the risks already live within its workflows.</p><p>Leaders must guide the shift from “move fast” to “move smart.”<br>That means fostering innovation while enforcing ethical, transparent, and compliant use of AI. The strongest AI-driven enterprises will be those that govern innovation well, not those that simply adopt it first.</p><p><strong>The Next Step: Enable Innovation Without the Shadow AI</strong></p><p>The rise of Shadow AI marks a pivotal moment in enterprise governance.</p><p>If left unchecked, it can undermine compliance, trust, and data integrity.<br>If governed effectively, it becomes a powerful catalyst for secure, compliant, and scalable innovation.</p><p>To help business leaders balance control with creativity, DivIHN Integration, Inc. invites you to an exclusive webinar:</p><p><strong>“Enable Innovation Without the Shadow AI”</strong></p><p>Join industry experts and technology leaders as they share actionable strategies for governing AI responsibly, protecting data, and accelerating enterprise innovation through secure, managed adoption.</p><p><strong>Date:</strong> Thursday, November 13, 2025<br><strong>Time:</strong> 11:00 AM to 12:00 PM CT</p>','','full_html'),('blog',0,131,822,'en',0,'<p>Across industries, employees are quietly embracing generative and agentic AI to work faster and smarter, using chatbots to draft content, summarize data, and even code through “vibe coding.”</p><p>A recent KPMG study reveals that 58% of employees now use AI tools daily, often because they outperform enterprise systems. But speed without oversight creates exposure. Nearly half admit to uploading sensitive company data to unauthorized platforms. What began as a productivity boost is quickly becoming a hidden security threat operating inside the firewall.</p><p><strong>Potential Security Threats Executives Should Not Ignore</strong></p><p>Shadow AI does not attack from the outside; it emerges quietly within enterprise workflows, making it especially dangerous Below are the most pressing threats organizations face today:</p><img src=\"/sites/default/files/inline-images/Blog_Creative_image_1.png\" data-entity-uuid=\"84c0384c-25a0-4401-a498-140829c1cbe8\" data-entity-type=\"file\" alt=\"Top 5 Risks executives should now\" width=\"879\" height=\"210\"><ol><li><p><strong>Sensitive Data Leakage and Exposure</strong></p><p><br>Employees often paste confidential information, customer details, codes, and strategy documents into public AI tools. That data may be retained by third-party systems, creating irreversible exposure with no audit trail.</p></li><li><p><strong>AI-Powered Attack Amplification</strong></p><p><br>Leaked outputs from unsanctioned AI use can be repurposed by adversaries to design far more convincing attacks. Threat actors can build tailored phishing, voice, or video impersonations and social-engineering schemes that closely mirror an organization’s tone and procedures, which increases the likelihood of successful compromise.</p></li><li><p><strong>Compliance Violations</strong></p><p>Shadow AI can breach data privacy laws such as GDPR, HIPAA, or SOC 2. Because these tools operate outside corporate compliance frameworks, they bypass consent, retention, and auditing controls.</p></li><li><p><strong>Model Manipulation and Bias Risks</strong></p><p><br>Unverified AI models can generate inaccurate, biased, or misleading outputs that influence decisions, from HR screening to financial projections. Without governance, these models can quietly distort operations.</p></li><li><p><strong>Data Exfiltration at Scale</strong></p><p><br>Shadow AI does not just leak data; it learns from it. Each prompt containing proprietary details reveals how teams work, decide, and think. Over time, these interactions can expose an organization’s logic, strategy, and IP, creating a continuous flow of insight leaving the enterprise without detection.</p><p>These risks are no longer theoretical; they represent an evolving cyber threat vector that traditional controls cannot detect or mitigate.</p></li></ol><p><strong>The Hidden Cost of Unchecked AI Use</strong></p><p>The danger of Shadow AI extends beyond cost exposure; it is a governance gap that undermines trust and accountability.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative2.png\" data-entity-uuid=\"c488f7b8-5bee-430d-a024-9f155f60479c\" data-entity-type=\"file\" alt=\"vertice 2025 source \" width=\"423\" height=\"466\"></p><p>AI models trained on uncontrolled data can produce outputs that distort business logic or introduce regulatory violations. Worse, when AI operates outside enterprise oversight, compliance teams lose visibility into data lineage, model reliability, and decision integrity.</p><p>Unverified external servers may store a single query containing sensitive information indefinitely, thereby exposing the organization to financial, legal, and reputational risk.</p><p>In the era of accelerated AI adoption, an unmanaged AI ecosystem has become the new insider threat.</p><p><strong>Building a Governance Model for Shadow AI</strong></p><p>Employees use unapproved AI tools to solve real problems, not to bypass policy. Forward-thinking leaders can transform this behavior into a managed advantage.</p><p>Build secure AI sandboxes where teams can safely experiment using anonymized or synthetic data. Deploy AI-aware data loss prevention (DLP) and cloud access security broker (CASB) solutions to improve visibility. Classify tools as approved, restricted, or prohibited based on assessed risk.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative3.png\" data-entity-uuid=\"68534c99-42ee-4afe-be6e-08ef92c3da3d\" data-entity-type=\"file\" alt=\"Vertice 2025 Source\" width=\"570\" height=\"403\"></p><p>Shift the mindset from enforcement to enablement by promoting transparency, open reporting, and continuous training. Learn from employee innovation patterns to accelerate enterprise AI adoption securely.</p><p>Shadow AI cannot be eliminated, but it can be governed. Effective governance is not restriction; it is about creating guardrails where innovation can thrive responsibly.</p><p><strong>Leadership’s Role: From Awareness to Action</strong></p><p>For today’s C-suite, Shadow AI is not just a technical challenge; it is a strategic and cultural one.</p><p>Every decision carries a digital dependency, and every digital process leaves an AI footprint. Whether an organization embraces AI or hesitates, the risks already live within its workflows.</p><p>Leaders must guide the shift from “move fast” to “move smart.”<br>That means fostering innovation while enforcing ethical, transparent, and compliant use of AI. The strongest AI-driven enterprises will be those that govern innovation well, not those that simply adopt it first.</p><p><strong>The Next Step: Enable Innovation Without the Shadow AI</strong></p><p>The rise of Shadow AI marks a pivotal moment in enterprise governance.</p><p>If left unchecked, it can undermine compliance, trust, and data integrity.<br>If governed effectively, it becomes a powerful catalyst for secure, compliant, and scalable innovation.</p><p>To help business leaders balance control with creativity, DivIHN Integration, Inc. invites you to an exclusive webinar:</p><p><strong>“Enable Innovation Without the Shadow AI”</strong></p><p>Join industry experts and technology leaders as they share actionable strategies for governing AI responsibly, protecting data, and accelerating enterprise innovation through secure, managed adoption.</p><p><strong>Date:</strong> Thursday, November 13, 2025<br><strong>Time:</strong> 11:00 AM to 12:00 PM CT</p>','','full_html'),('blog',0,131,828,'en',0,'<p>Across industries, employees are quietly embracing generative and agentic AI to work faster and smarter, using chatbots to draft content, summarize data, and even code through “vibe coding.”</p><p>A recent KPMG study reveals that 58% of employees now use AI tools daily, often because they outperform enterprise systems. But speed without oversight creates exposure. Nearly half admit to uploading sensitive company data to unauthorized platforms. What began as a productivity boost is quickly becoming a hidden security threat operating inside the firewall.</p><p><strong>Potential Security Threats Executives Should Not Ignore</strong></p><p>Shadow AI does not attack from the outside; it emerges quietly within enterprise workflows, making it especially dangerous Below are the most pressing threats organizations face today:</p><img src=\"/sites/default/files/inline-images/Blog_Creative_image_1.png\" data-entity-uuid=\"84c0384c-25a0-4401-a498-140829c1cbe8\" data-entity-type=\"file\" alt=\"Top 5 Risks executives should now\" width=\"879\" height=\"210\"><ol><li><p><strong>Sensitive Data Leakage and Exposure</strong></p><p><br>Employees often paste confidential information, customer details, codes, and strategy documents into public AI tools. That data may be retained by third-party systems, creating irreversible exposure with no audit trail.</p></li><li><p><strong>AI-Powered Attack Amplification</strong></p><p><br>Leaked outputs from unsanctioned AI use can be repurposed by adversaries to design far more convincing attacks. Threat actors can build tailored phishing, voice, or video impersonations and social-engineering schemes that closely mirror an organization’s tone and procedures, which increases the likelihood of successful compromise.</p></li><li><p><strong>Compliance Violations</strong></p><p>Shadow AI can breach data privacy laws such as GDPR, HIPAA, or SOC 2. Because these tools operate outside corporate compliance frameworks, they bypass consent, retention, and auditing controls.</p></li><li><p><strong>Model Manipulation and Bias Risks</strong></p><p><br>Unverified AI models can generate inaccurate, biased, or misleading outputs that influence decisions, from HR screening to financial projections. Without governance, these models can quietly distort operations.</p></li><li><p><strong>Data Exfiltration at Scale</strong></p><p><br>Shadow AI does not just leak data; it learns from it. Each prompt containing proprietary details reveals how teams work, decide, and think. Over time, these interactions can expose an organization’s logic, strategy, and IP, creating a continuous flow of insight leaving the enterprise without detection.</p><p>These risks are no longer theoretical; they represent an evolving cyber threat vector that traditional controls cannot detect or mitigate.</p></li></ol><p><strong>The Hidden Cost of Unchecked AI Use</strong></p><p>The danger of Shadow AI extends beyond cost exposure; it is a governance gap that undermines trust and accountability.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative2.png\" data-entity-uuid=\"c488f7b8-5bee-430d-a024-9f155f60479c\" data-entity-type=\"file\" alt=\"vertice 2025 source \" width=\"423\" height=\"466\"></p><p>AI models trained on uncontrolled data can produce outputs that distort business logic or introduce regulatory violations. Worse, when AI operates outside enterprise oversight, compliance teams lose visibility into data lineage, model reliability, and decision integrity.</p><p>Unverified external servers may store a single query containing sensitive information indefinitely, thereby exposing the organization to financial, legal, and reputational risk.</p><p>In the era of accelerated AI adoption, an unmanaged AI ecosystem has become the new insider threat.</p><p><strong>Building a Governance Model for Shadow AI</strong></p><p>Employees use unapproved AI tools to solve real problems, not to bypass policy. Forward-thinking leaders can transform this behavior into a managed advantage.</p><p>Build secure AI sandboxes where teams can safely experiment using anonymized or synthetic data. Deploy AI-aware data loss prevention (DLP) and cloud access security broker (CASB) solutions to improve visibility. Classify tools as approved, restricted, or prohibited based on assessed risk.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative3.png\" data-entity-uuid=\"68534c99-42ee-4afe-be6e-08ef92c3da3d\" data-entity-type=\"file\" alt=\"Vertice 2025 Source\" width=\"570\" height=\"403\"></p><p>Shift the mindset from enforcement to enablement by promoting transparency, open reporting, and continuous training. Learn from employee innovation patterns to accelerate enterprise AI adoption securely.</p><p>Shadow AI cannot be eliminated, but it can be governed. Effective governance is not restriction; it is about creating guardrails where innovation can thrive responsibly.</p><p><strong>Leadership’s Role: From Awareness to Action</strong></p><p>For today’s C-suite, Shadow AI is not just a technical challenge; it is a strategic and cultural one.</p><p>Every decision carries a digital dependency, and every digital process leaves an AI footprint. Whether an organization embraces AI or hesitates, the risks already live within its workflows.</p><p>Leaders must guide the shift from “move fast” to “move smart.”<br>That means fostering innovation while enforcing ethical, transparent, and compliant use of AI. The strongest AI-driven enterprises will be those that govern innovation well, not those that simply adopt it first.</p><p><strong>The Next Step: Enable Innovation Without the Shadow AI</strong></p><p>The rise of Shadow AI marks a pivotal moment in enterprise governance.</p><p>If left unchecked, it can undermine compliance, trust, and data integrity.<br>If governed effectively, it becomes a powerful catalyst for secure, compliant, and scalable innovation.</p><p>To help business leaders balance control with creativity, DivIHN Integration, Inc. invites you to an exclusive webinar:</p><p><strong>“Enable Innovation Without the Shadow AI”</strong></p><p>Join industry experts and technology leaders as they share actionable strategies for governing AI responsibly, protecting data, and accelerating enterprise innovation through secure, managed adoption.</p><p><strong>Date:</strong> Thursday, November 13, 2025<br><strong>Time:</strong> 11:00 AM to 12:00 PM CT</p>','','full_html'),('blog',0,131,829,'en',0,'<p>Across industries, employees are quietly embracing generative and agentic AI to work faster and smarter, using chatbots to draft content, summarize data, and even code through “vibe coding.”</p><p>A recent KPMG study reveals that 58% of employees now use AI tools daily, often because they outperform enterprise systems. But speed without oversight creates exposure. Nearly half admit to uploading sensitive company data to unauthorized platforms. What began as a productivity boost is quickly becoming a hidden security threat operating inside the firewall.</p><p><strong>Potential Security Threats Executives Should Not Ignore</strong></p><p>Shadow AI does not attack from the outside; it emerges quietly within enterprise workflows, making it especially dangerous Below are the most pressing threats organizations face today:</p><img src=\"/sites/default/files/inline-images/Blog_Creative_image_1.png\" data-entity-uuid=\"84c0384c-25a0-4401-a498-140829c1cbe8\" data-entity-type=\"file\" alt=\"Top 5 Risks executives should now\" width=\"879\" height=\"210\"><ol><li><p><strong>Sensitive Data Leakage and Exposure</strong></p><p><br>Employees often paste confidential information, customer details, codes, and strategy documents into public AI tools. That data may be retained by third-party systems, creating irreversible exposure with no audit trail.</p></li><li><p><strong>AI-Powered Attack Amplification</strong></p><p><br>Leaked outputs from unsanctioned AI use can be repurposed by adversaries to design far more convincing attacks. Threat actors can build tailored phishing, voice, or video impersonations and social-engineering schemes that closely mirror an organization’s tone and procedures, which increases the likelihood of successful compromise.</p></li><li><p><strong>Compliance Violations</strong></p><p>Shadow AI can breach data privacy laws such as GDPR, HIPAA, or SOC 2. Because these tools operate outside corporate compliance frameworks, they bypass consent, retention, and auditing controls.</p></li><li><p><strong>Model Manipulation and Bias Risks</strong></p><p><br>Unverified AI models can generate inaccurate, biased, or misleading outputs that influence decisions, from HR screening to financial projections. Without governance, these models can quietly distort operations.</p></li><li><p><strong>Data Exfiltration at Scale</strong></p><p><br>Shadow AI does not just leak data; it learns from it. Each prompt containing proprietary details reveals how teams work, decide, and think. Over time, these interactions can expose an organization’s logic, strategy, and IP, creating a continuous flow of insight leaving the enterprise without detection.</p><p>These risks are no longer theoretical; they represent an evolving cyber threat vector that traditional controls cannot detect or mitigate.</p></li></ol><p><strong>The Hidden Cost of Unchecked AI Use</strong></p><p>The danger of Shadow AI extends beyond cost exposure; it is a governance gap that undermines trust and accountability.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative2.png\" data-entity-uuid=\"c488f7b8-5bee-430d-a024-9f155f60479c\" data-entity-type=\"file\" alt=\"vertice 2025 source \" width=\"423\" height=\"466\"></p><p>AI models trained on uncontrolled data can produce outputs that distort business logic or introduce regulatory violations. Worse, when AI operates outside enterprise oversight, compliance teams lose visibility into data lineage, model reliability, and decision integrity.</p><p>Unverified external servers may store a single query containing sensitive information indefinitely, thereby exposing the organization to financial, legal, and reputational risk.</p><p>In the era of accelerated AI adoption, an unmanaged AI ecosystem has become the new insider threat.</p><p><strong>Building a Governance Model for Shadow AI</strong></p><p>Employees use unapproved AI tools to solve real problems, not to bypass policy. Forward-thinking leaders can transform this behavior into a managed advantage.</p><p>Build secure AI sandboxes where teams can safely experiment using anonymized or synthetic data. Deploy AI-aware data loss prevention (DLP) and cloud access security broker (CASB) solutions to improve visibility. Classify tools as approved, restricted, or prohibited based on assessed risk.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative3.png\" data-entity-uuid=\"68534c99-42ee-4afe-be6e-08ef92c3da3d\" data-entity-type=\"file\" alt=\"Vertice 2025 Source\" width=\"570\" height=\"403\"></p><p>Shift the mindset from enforcement to enablement by promoting transparency, open reporting, and continuous training. Learn from employee innovation patterns to accelerate enterprise AI adoption securely.</p><p>Shadow AI cannot be eliminated, but it can be governed. Effective governance is not restriction; it is about creating guardrails where innovation can thrive responsibly.</p><p><strong>Leadership’s Role: From Awareness to Action</strong></p><p>For today’s C-suite, Shadow AI is not just a technical challenge; it is a strategic and cultural one.</p><p>Every decision carries a digital dependency, and every digital process leaves an AI footprint. Whether an organization embraces AI or hesitates, the risks already live within its workflows.</p><p>Leaders must guide the shift from “move fast” to “move smart.”<br>That means fostering innovation while enforcing ethical, transparent, and compliant use of AI. The strongest AI-driven enterprises will be those that govern innovation well, not those that simply adopt it first.</p><p><strong>The Next Step: Enable Innovation Without the Shadow AI</strong></p><p>The rise of Shadow AI marks a pivotal moment in enterprise governance.</p><p>If left unchecked, it can undermine compliance, trust, and data integrity.<br>If governed effectively, it becomes a powerful catalyst for secure, compliant, and scalable innovation.</p><p>To help business leaders balance control with creativity, DivIHN Integration, Inc. invites you to an exclusive webinar:</p><p><strong>“Enable Innovation Without the Shadow AI”</strong></p><p>Join industry experts and technology leaders as they share actionable strategies for governing AI responsibly, protecting data, and accelerating enterprise innovation through secure, managed adoption.</p><p><strong>Date:</strong> Thursday, November 13, 2025<br><strong>Time:</strong> 11:00 AM to 12:00 PM CT</p>','','full_html'),('blog',0,131,830,'en',0,'<p>Across industries, employees are quietly embracing generative and agentic AI to work faster and smarter, using chatbots to draft content, summarize data, and even code through “vibe coding.”</p><p>A recent KPMG study reveals that 58% of employees now use AI tools daily, often because they outperform enterprise systems. But speed without oversight creates exposure. Nearly half admit to uploading sensitive company data to unauthorized platforms. What began as a productivity boost is quickly becoming a hidden security threat operating inside the firewall.</p><p><strong>Potential Security Threats Executives Should Not Ignore</strong></p><p>Shadow AI does not attack from the outside; it emerges quietly within enterprise workflows, making it especially dangerous Below are the most pressing threats organizations face today:</p><img src=\"/sites/default/files/inline-images/Blog_Creative_image_1.png\" data-entity-uuid=\"84c0384c-25a0-4401-a498-140829c1cbe8\" data-entity-type=\"file\" alt=\"Top 5 Risks executives should now\" width=\"879\" height=\"210\"><ol><li><p><strong>Sensitive Data Leakage and Exposure</strong></p><p><br>Employees often paste confidential information, customer details, codes, and strategy documents into public AI tools. That data may be retained by third-party systems, creating irreversible exposure with no audit trail.</p></li><li><p><strong>AI-Powered Attack Amplification</strong></p><p><br>Leaked outputs from unsanctioned AI use can be repurposed by adversaries to design far more convincing attacks. Threat actors can build tailored phishing, voice, or video impersonations and social-engineering schemes that closely mirror an organization’s tone and procedures, which increases the likelihood of successful compromise.</p></li><li><p><strong>Compliance Violations</strong></p><p>Shadow AI can breach data privacy laws such as GDPR, HIPAA, or SOC 2. Because these tools operate outside corporate compliance frameworks, they bypass consent, retention, and auditing controls.</p></li><li><p><strong>Model Manipulation and Bias Risks</strong></p><p><br>Unverified AI models can generate inaccurate, biased, or misleading outputs that influence decisions, from HR screening to financial projections. Without governance, these models can quietly distort operations.</p></li><li><p><strong>Data Exfiltration at Scale</strong></p><p><br>Shadow AI does not just leak data; it learns from it. Each prompt containing proprietary details reveals how teams work, decide, and think. Over time, these interactions can expose an organization’s logic, strategy, and IP, creating a continuous flow of insight leaving the enterprise without detection.</p><p>These risks are no longer theoretical; they represent an evolving cyber threat vector that traditional controls cannot detect or mitigate.</p></li></ol><p><strong>The Hidden Cost of Unchecked AI Use</strong></p><p>The danger of Shadow AI extends beyond cost exposure; it is a governance gap that undermines trust and accountability.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative2.png\" data-entity-uuid=\"c488f7b8-5bee-430d-a024-9f155f60479c\" data-entity-type=\"file\" alt=\"vertice 2025 source \" width=\"423\" height=\"466\"></p><p>AI models trained on uncontrolled data can produce outputs that distort business logic or introduce regulatory violations. Worse, when AI operates outside enterprise oversight, compliance teams lose visibility into data lineage, model reliability, and decision integrity.</p><p>Unverified external servers may store a single query containing sensitive information indefinitely, thereby exposing the organization to financial, legal, and reputational risk.</p><p>In the era of accelerated AI adoption, an unmanaged AI ecosystem has become the new insider threat.</p><p><strong>Building a Governance Model for Shadow AI</strong></p><p>Employees use unapproved AI tools to solve real problems, not to bypass policy. Forward-thinking leaders can transform this behavior into a managed advantage.</p><p>Build secure AI sandboxes where teams can safely experiment using anonymized or synthetic data. Deploy AI-aware data loss prevention (DLP) and cloud access security broker (CASB) solutions to improve visibility. Classify tools as approved, restricted, or prohibited based on assessed risk.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative3.png\" data-entity-uuid=\"68534c99-42ee-4afe-be6e-08ef92c3da3d\" data-entity-type=\"file\" alt=\"Vertice 2025 Source\" width=\"570\" height=\"403\"></p><p>Shift the mindset from enforcement to enablement by promoting transparency, open reporting, and continuous training. Learn from employee innovation patterns to accelerate enterprise AI adoption securely.</p><p>Shadow AI cannot be eliminated, but it can be governed. Effective governance is not restriction; it is about creating guardrails where innovation can thrive responsibly.</p><p><strong>Leadership’s Role: From Awareness to Action</strong></p><p>For today’s C-suite, Shadow AI is not just a technical challenge; it is a strategic and cultural one.</p><p>Every decision carries a digital dependency, and every digital process leaves an AI footprint. Whether an organization embraces AI or hesitates, the risks already live within its workflows.</p><p>Leaders must guide the shift from “move fast” to “move smart.”<br>That means fostering innovation while enforcing ethical, transparent, and compliant use of AI. The strongest AI-driven enterprises will be those that govern innovation well, not those that simply adopt it first.</p><p><strong>The Next Step: Enable Innovation Without the Shadow AI</strong></p><p>The rise of Shadow AI marks a pivotal moment in enterprise governance.</p><p>If left unchecked, it can undermine compliance, trust, and data integrity.<br>If governed effectively, it becomes a powerful catalyst for secure, compliant, and scalable innovation.</p><p>To help business leaders balance control with creativity, DivIHN Integration, Inc. invites you to an exclusive webinar:</p><p><strong>“Enable Innovation Without the Shadow AI”</strong></p><p>Join industry experts and technology leaders as they share actionable strategies for governing AI responsibly, protecting data, and accelerating enterprise innovation through secure, managed adoption.</p><p><strong>Date:</strong> Thursday, November 13, 2025<br><strong>Time:</strong> 11:00 AM to 12:00 PM CT</p>','','full_html'),('blog',0,131,831,'en',0,'<p>Across industries, employees are quietly embracing generative and agentic AI to work faster and smarter, using chatbots to draft content, summarize data, and even code through “vibe coding.”</p><p>A recent KPMG study reveals that 58% of employees now use AI tools daily, often because they outperform enterprise systems. But speed without oversight creates exposure. Nearly half admit to uploading sensitive company data to unauthorized platforms. What began as a productivity boost is quickly becoming a hidden security threat operating inside the firewall.</p><p><strong>Potential Security Threats Executives Should Not Ignore</strong></p><p>Shadow AI does not attack from the outside; it emerges quietly within enterprise workflows, making it especially dangerous Below are the most pressing threats organizations face today:</p><img src=\"/sites/default/files/inline-images/Blog_Creative_image_1.png\" data-entity-uuid=\"84c0384c-25a0-4401-a498-140829c1cbe8\" data-entity-type=\"file\" alt=\"Top 5 Risks executives should now\" width=\"879\" height=\"210\"><ol><li><p><strong>Sensitive Data Leakage and Exposure</strong></p><p><br>Employees often paste confidential information, customer details, codes, and strategy documents into public AI tools. That data may be retained by third-party systems, creating irreversible exposure with no audit trail.</p></li><li><p><strong>AI-Powered Attack Amplification</strong></p><p><br>Leaked outputs from unsanctioned AI use can be repurposed by adversaries to design far more convincing attacks. Threat actors can build tailored phishing, voice, or video impersonations and social-engineering schemes that closely mirror an organization’s tone and procedures, which increases the likelihood of successful compromise.</p></li><li><p><strong>Compliance Violations</strong></p><p>Shadow AI can breach data privacy laws such as GDPR, HIPAA, or SOC 2. Because these tools operate outside corporate compliance frameworks, they bypass consent, retention, and auditing controls.</p></li><li><p><strong>Model Manipulation and Bias Risks</strong></p><p><br>Unverified AI models can generate inaccurate, biased, or misleading outputs that influence decisions, from HR screening to financial projections. Without governance, these models can quietly distort operations.</p></li><li><p><strong>Data Exfiltration at Scale</strong></p><p><br>Shadow AI does not just leak data; it learns from it. Each prompt containing proprietary details reveals how teams work, decide, and think. Over time, these interactions can expose an organization’s logic, strategy, and IP, creating a continuous flow of insight leaving the enterprise without detection.</p><p>These risks are no longer theoretical; they represent an evolving cyber threat vector that traditional controls cannot detect or mitigate.</p></li></ol><p><strong>The Hidden Cost of Unchecked AI Use</strong></p><p>The danger of Shadow AI extends beyond cost exposure; it is a governance gap that undermines trust and accountability.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative2.png\" data-entity-uuid=\"c488f7b8-5bee-430d-a024-9f155f60479c\" data-entity-type=\"file\" alt=\"vertice 2025 source \" width=\"423\" height=\"466\"></p><p>AI models trained on uncontrolled data can produce outputs that distort business logic or introduce regulatory violations. Worse, when AI operates outside enterprise oversight, compliance teams lose visibility into data lineage, model reliability, and decision integrity.</p><p>Unverified external servers may store a single query containing sensitive information indefinitely, thereby exposing the organization to financial, legal, and reputational risk.</p><p>In the era of accelerated AI adoption, an unmanaged AI ecosystem has become the new insider threat.</p><p><strong>Building a Governance Model for Shadow AI</strong></p><p>Employees use unapproved AI tools to solve real problems, not to bypass policy. Forward-thinking leaders can transform this behavior into a managed advantage.</p><p>Build secure AI sandboxes where teams can safely experiment using anonymized or synthetic data. Deploy AI-aware data loss prevention (DLP) and cloud access security broker (CASB) solutions to improve visibility. Classify tools as approved, restricted, or prohibited based on assessed risk.</p><p><img src=\"/sites/default/files/inline-images/BlogCreative3.png\" data-entity-uuid=\"68534c99-42ee-4afe-be6e-08ef92c3da3d\" data-entity-type=\"file\" alt=\"Vertice 2025 Source\" width=\"570\" height=\"403\"></p><p>Shift the mindset from enforcement to enablement by promoting transparency, open reporting, and continuous training. Learn from employee innovation patterns to accelerate enterprise AI adoption securely.</p><p>Shadow AI cannot be eliminated, but it can be governed. Effective governance is not restriction; it is about creating guardrails where innovation can thrive responsibly.</p><p><strong>Leadership’s Role: From Awareness to Action</strong></p><p>For today’s C-suite, Shadow AI is not just a technical challenge; it is a strategic and cultural one.</p><p>Every decision carries a digital dependency, and every digital process leaves an AI footprint. Whether an organization embraces AI or hesitates, the risks already live within its workflows.</p><p>Leaders must guide the shift from “move fast” to “move smart.”<br>That means fostering innovation while enforcing ethical, transparent, and compliant use of AI. The strongest AI-driven enterprises will be those that govern innovation well, not those that simply adopt it first.</p><p><strong>The Next Step: Enable Innovation Without the Shadow AI</strong></p><p>The rise of Shadow AI marks a pivotal moment in enterprise governance.</p><p>If left unchecked, it can undermine compliance, trust, and data integrity.<br>If governed effectively, it becomes a powerful catalyst for secure, compliant, and scalable innovation.</p><p>To help business leaders balance control with creativity, DivIHN Integration, Inc. invites you to an exclusive webinar:</p><p><strong>“Enable Innovation Without the Shadow AI”</strong></p><p>Join industry experts and technology leaders as they share actionable strategies for governing AI responsibly, protecting data, and accelerating enterprise innovation through secure, managed adoption.</p><p><strong>Date:</strong> Thursday, November 13, 2025<br><strong>Time:</strong> 11:00 AM to 12:00 PM CT</p>','','full_html'),('webinar',0,132,603,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,132,676,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,132,706,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,132,707,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,132,708,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,132,709,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('webinar',0,132,730,'en',0,'<p><strong>About Webinar</strong></p><p>AI is evolving into more autonomous, “agentic” systems capable of making independent decisions. This change is transforming the security landscape. For CISOs and security leaders, it means reevaluating strategies to balance innovation and risk. In this panel discussion, industry experts will share real-world use cases and practical ways to leverage Agentic AI for stronger defenses, improved threat detection, and resilient operations.</p>','','full_html'),('jobs',0,133,677,'en',0,'<p>Sample</p>','','basic_html');

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