Organizational leadership faces a critical decision: apply familiar commodity IT approaches to AI development The wrong choice creates cascading risks that compound as AI systems learn and adapt in unpredictable AI Systems violate every assumption that makes commodity IT approaches successful. Organizational Capability determines long-term success in AI deployment. systems that becomes increasingly valuable as AI adoption scales.

Artificial Intelligence (AI) risk databases are growing, AI risk taxonomies and classifications are expanding , and AI risk registers are being created and added to at an accelerated rate. Here are a few resources that are attempting to capture them: AI Risk Repository by MIT [ https://airisk.mit.edu /](https://airisk.mit.edu/) AI Risk Database - [ https://airisk.io/](https://airisk.io/) Unfortunately There are not enough brooms to push back the tsunami of AI risk.

But here's what I discovered after reviewing AI implementation plans: most aren't actually about AI at , and tools to rapidly deploy AI Grow an AI -first workforce to oversee and integrate AI throughout That's the problem. ⚡ Why This Matters Real AI strategy requires addressing AI-specific challenges that generic technology that happens to be called AI. ⚡ AI Isn't Ketchup Too many organizations treat AI But AI isn't ketchup.

A recent IEEE webinar that I attended on AI Safety for Automotive provided valuable insights into the upcoming ISO PAS 8800 standard, introducing a pragmatic approach to AI safety assurance that I believe The webinar presented what I'll call the "Requirements Isolation Strategy" - a methodical approach to AI requirements that are allocated to AI functionality. What strategies are you using to advance AI Safety within your operations and systems?

Understanding the Risks Third-party AI risks arise when the AI systems, algorithms, or data used by external Steps for Managing Third-Party AI Risks Identify and Assess Third-Party AI Dependencies Start by creating a comprehensive inventory of all third-party partners who use AI or provide AI-enabled services. Conduct Regular AI Risk Audits Periodically assess your third parties’ compliance with your AI standards of responsible AI practices.

As AI systems are increasingly embedded in critical functions across industries, ensuring their reliability This approach could offer a robust foundation for ongoing AI performance management. 2. frameworks tailored to AI’s unique vulnerabilities. layer, safeguarding AI systems against intentional and unintentional security risks. 3. could form the basis of a future AI assurance framework.

AI does not. And AI doesn't tell you which is which. The procedure AI returns may be close to the one you need, or synthetic — created in real time. For AI to be reliable in the enterprise, it has to do what full-text search never had to: handle both

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

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

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.