Unlike static technologies, AI systems continuously learn and evolve, rendering traditional regulatory What this means in practice is that to regulate artificial intelligence, compliance must also be intelligent This means effective AI safety cannot be reduced to a simple label based on an assessment that happened Learning from other Domains Software as a Medical Device (SaMD) The Software as a Medical Device (SaMD However, this approach directly conflicts with AI's core value proposition – its ability to learn, adapt

This means being careful about where your data comes from, protecting privacy, and being open about how professionals are moving into AI governance roles, but fewer are pursuing AI engineering due to the longer lead challenge is more pervasive than previous BYOD issues, as AI tools are easily accessible online and often leave This highlights how AI governance is emerging from IT but needs to learn from industries with long histories

The bridge between them is where models learn from enterprise information and where intelligence acts

Your governance needs to learn and adapt, or it becomes irrelevant quickly. We build learning systems around the opacity rather than trying to eliminate it. working through these ideas in more detail—how cybernetic principles apply to AI governance, what this means

This has much to do about learning and improving our defences to make them more robust. airline industry has a strong safety record partly because after every incident they took a deep dive and learned They did not waste any knowledge that could be learned from disasters. We need to prevent non-conformance, recover from them should they occur, and get stronger when we learn

Operational Rings of Power These are held together by the fellowship of: 🔸 Feed Forward Processes - leading

together, improve the probability of success by governing, regulating, and ensuring the ends and the means If you're interested in learning more about transforming reactive GRC functions into proactive GRE capabilities

We learn through observation and adaptation rather than pre-planning. 4. plan for every contingency, successful AI engineering embraces iterative development with continuous learning Because these systems continuously learn, adapt, and evolve, yesterday's test results may not predict In practical terms, this means developing systems where: We can reasonably predict the boundaries of aspects of system performance We can make evidence-based improvements based on real-world operation Learning

, it is just an improvement in productivity, and they question whether the use of AI assistants may lead No wonder there is a fear that the widespread use of AI assistants may lead to significant job reductions

This approach is also not without its problems as it can sometimes lead to "blaming" the system and loss Root Cause Analysis Dean Gano, the creator of the Apollo Root Cause Analysis method [2][3], goes back This leads to the conclusion that a cause and an effect are the same thing.

operational or enterprise systems introduces unacceptable risks, as even minor algorithmic errors can lead

One of the powers of technology is its ability to externalize the means to achieve our ends. It is externalizing the means by which we learn to the point that we don’t need to learn ourselves. What if meaning is found not by having the goal of our desire but instead by our participation in the means to make it happen.