the kind we make at year-end meetings, but the deeper promises organizations make when they deploy AI A Cybernetic Approach to AI Compliance Two insights have been particularly valuable: First, trying to This changes how I think about AI governance. in regulated environments, this offers a more realistic path forward than waiting for "explainable AI I've been working through these ideas in more detail—how cybernetic principles apply to AI governance

As a compliance engineer, I've watched AI transform from research curiosity to world-changing technology The Sustainability Dilemma The resource demands of advanced AI are staggering. Medical professionals may lose diagnostic skills when relying heavily on AI. Not every process needs AI—sometimes simpler solutions are both sufficient and sustainable. Promote Accessible AI Infrastructure Support initiatives creating public AI resources and open-source

Many organizations are embracing AI to advance their goals. However, ensuring the public's well-being requires AI practices to meet three critical conditions: Legality : AI development and use must comply with relevant laws and regulations, safeguarding fundamental rights Ethical Alignment : AI practices must adhere to ethical principles and established moral standards. Societal Benefit: AI applications should be demonstrably beneficial, improving the lives of individuals

greater accuracy, an unexpected phenomenon is emerging: the convergence of responses across different AI This trend raises concerns about the potential loss of diverse perspectives in AI-generated content. Have you noticed that when posing questions to various generative AI applications like ChatGPT, Gemini Model convergence occurs when multiple AI models, despite being developed by different organizations, we maintain intellectual diversity in AI-generated content?

For AI safety analysis, STAMP/STPA appears better suited to AI's systemic and emergent risks, but the choice becomes more nuanced when considering AI's integration into traditional process systems.  The real challenge lies in analyzing AI-augmented process control systems—where an AI controller making Rather than viewing these as competing methodologies, the most thoughtful approach recognizes that AI as AI becomes embedded throughout industrial infrastructure.

When Words are Not Enough: The Limitations of AI In the race toward artificial general intelligence, AI systems can now analyze research papers at unprecedented speeds, identify patterns in complex datasets Yet, there's a fundamental limitation in this approach that we must acknowledge: AI systems don't directly As we continue to advance AI technology, we must remain mindful of these limitations. While AI represents a powerful tool for processing and analyzing human knowledge, it cannot replace the

three decades of experience in highly regulated industries, I firmly believe we can and should embrace AI troubling pattern: organizations are bypassing the engineering design phase and directly jumping from AI This “Deploy First, Engineer Later” approach or as some call, "Fail First, Fail Fast": treats AI systems When we want these qualities in AI systems and the internal controls that use them, we must engineer However, AI is being integrated into these systems without this essential engineering work.

As we push the boundaries of technological innovation, we're witnessing a fascinating progression of AI The Learning Foundation: Machine Learning Systems Imagine an AI that can learn from past experiences, Where machine learning sees patterns, AI systems see stories, connections, and deeper meanings. Autonomous Action: Agentic AI Systems The plot thickens with Agentic AI Systems – the problem-solvers The Frontier of Self-Evolution: Autopoietic AI Systems Here's where things get truly mind-bending.

) there is one question that must be answered, "What is AI?" At one level AI consists of the same computing technology we have used in the past. How should AI be best defined? The same principle applies to AI. More work is needed to develop clarity to what AI is and what it is not.

AI Assurance isn't just about checking boxes before deployment. With today's AI systems, we simply can't predict everything in advance—we need to stay vigilant while In the paper published by the European Defence Agency (EDA), entitled “Trustworthiness for AI in Defence in Defence - Figure 14 The introduction of AI technologies and autonomy capabilities has tipped the adequate confidence and evidence that the AI system satisfies the intended function (System Assurance

In recent days, a thought-provoking statement was made suggesting that artificial intelligence (AI) should they create a cross-functional AI Ethics Committee to oversee the ethical implications of AI use within maintain ethical guidelines for AI development and deployment Provide guidance on complex AI-related ethical dilemmas Monitor emerging AI regulations and industry best practice. and explainability measures for AI-driven decisions Foster a culture of responsible AI use throughout

AI compliance demands a fundamentally new mindset. Neither alone is sufficient to ensure AI delivers real benefits in a safe and responsible manner. When it comes to AI, the stakes are exceptionally high, with both significant risks and opportunities This environment demands real-time AI governance, supported by programs, systems, and processes that Applying Lean Startup to AI Compliance in Practice The Lean Startup approach for AI compliance focuses