Bridge the gap between AI principles and practice. Learn how to use the NIST AI RMF for provable governance and combat costly AI hallucinations in your enterprise.

If you’re responsible for AI at any level, you need to move from 'policy' to 'provable governance.' It’s about moving from 'we hope our engineers read the policy' to 'the system won't let the engineers violate the policy.'
Artificial intelligence risk management







The principle-practice gap occurs when organizations establish high-level AI principles like fairness and transparency but fail to implement them within their software's code. As discussed in 'AI Risk: From Policy to Practice,' many companies struggle because their written policies do not actually control how their AI behaves in real-time. Closing this gap requires moving beyond aspirations toward the 'plumbing' of provable governance to ensure reliability.
AI hallucinations have become a crisis of reliability for modern enterprises, leading to over $67 billion in global losses in 2024 alone. Data shows that 47% of enterprise users have admitted to making major business decisions based on these hallucinations. This makes AI risk management a matter of basic business survival rather than just an ethical concern, as organizations must ensure their systems provide accurate and dependable outputs.
The NIST AI Risk Management Framework (AI RMF) provides a structured approach for organizations to move from theoretical policy to provable governance. By following this framework, businesses can better manage the risks associated with AI behavior and reliability. In 2026, the NIST AI RMF continues to evolve to help leaders bridge the gap between their stated AI principles and the actual technical implementation of their software.
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