{"id":35774,"date":"2025-01-30T13:12:51","date_gmt":"2025-01-30T13:12:51","guid":{"rendered":"https:\/\/digileaders.com\/?p=35774"},"modified":"2025-01-30T13:12:51","modified_gmt":"2025-01-30T13:12:51","slug":"establishing-a-path-to-secure-ai","status":"publish","type":"post","link":"https:\/\/digileaders.com\/establishing-a-path-to-secure-ai\/","title":{"rendered":"Establishing a path to secure AI"},"content":{"rendered":"

As AI adoption grows, ensuring AI systems are resilient is critical. This requires building user trust and ensuring the trustworthiness of deployed AI technology.<\/strong><\/p>\n

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We are now all very much aware that digital systems are vital to our lives and livelihoods. It\u2019s become impossible to imagine how we\u2019d get by without them. But we also know that digital systems can fail.\u00a0Sometimes catastrophically<\/a>. These failures, such as service outages and data breaches, have significant negative impacts on individuals, businesses, and society as a whole.<\/p>\n<\/div>\n

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It is with these thoughts in mind that I was delighted to provide the foreword to a new book that has been launched this week. \u201cResilience of Services: Reducing the Impact of IT Failures<\/a>\u201d brings together data and thinking from more than 200 experts to provide both an understanding of the consequences of failures in digital systems and an approach to reducing these failures and their impacts. Its authors, Gill Ringland and Ed Steinmueller, have deep expertise and many years of experience exploring resilience in IT systems. Their work has been significant in advancing our understanding of building better, more robust digital systems.<\/p>\n<\/div>\n

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The messages contained within the book are important for managing existing software-intensive systems. But, in talking with Gill and Ed, I realize that their work may have an even more significant role as organizations enter the age of AI. The enterprise adoption of AI has reached a critical point. As organizations move beyond experiments and small-scale solutions to integrate AI capabilities into core business operations and strategic decision-making processes, they face a key barrier holding back progress: How to ensure the security of AI.<\/p>\n<\/div>\n

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In talking with a wide range of people on resilience of AI systems, I have found that two distinct, but complementary challenges must be considered to make progress: Building user trust in AI systems and ensuring deployed AI technology is trustworthy.<\/p>\n

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Building user trust in AI systems<\/strong><\/p>\n<\/div>\n

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Public trust in AI is declining<\/a>, with many\u00a0expressing concerns about AI safety<\/a>. Building and maintaining user trust is crucial for widespread AI adoption.<\/p>\n<\/div>\n

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While many factors contribute to trust in AI, a\u00a0new report from the World Economic Forum (WEF)<\/a>\u00a0identifies three critical factors in building and maintaining trust in AI systems.<\/p>\n<\/div>\n

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Transparency as foundation<\/b><\/p>\n<\/div>\n

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According to the WEF report, the primary factor contributing to the trust deficit is the lack of transparency surrounding AI systems. Users often perceive AI as a “black box,” with limited understanding of how decisions are made, what data is used for training, and where potential biases might exist. Organizations must prioritize transparency in their AI development and deployment strategies, providing clear explanations of model functionality, data sources, and decision-making processes.<\/p>\n<\/div>\n

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Data privacy and security<\/b><\/p>\n<\/div>\n

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With increasing reliance on AI, organizations are processing larger volumes of sensitive data. The WEF report highlights this is driving rising user concerns about data privacy. As a result, robust data protection measures are critical in ensuring trust, including actions such as:<\/p>\n<\/div>\n

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