This mindset can lead to hasty decisions and a lack of consideration for the long-term consequences of This mindset can lead to risky behaviour and a lack of accountability. It means being willing to invest time, resources, and effort today for more substantial, enduring, and With respect to compliance this means taking ownership of obligations and keeping promises associated However, I think it means more than this. We must learn to think beyond ourselves.

Process safety practices have evolved over decades of experience and many lessons learned.

You need to learn all that’s essential for you to drive, and that means learning how things work together And this can only be learned by practising them at the same time. You will look forward with anticipation to the benefits you will experience because you have learned Why are we not expecting and using the entire program, and learn how to drive it so that it delivers But we first need to learn how to drive.

Learning reduces it. So does capability. This is uncertainty you buy down. Variation is part of real conditions, and no amount of learning removes it.

occur at the beginning of meetings or shifts and revolve around sharing personal experiences, lessons learned Learning from Incidents: Safety moments involve discussing near misses or incidents that have occurred vulnerabilities, limitations, and potential failure modes of AI systems, allowing organizations to learn This continuous learning approach helps build a resilient workforce capable of handling AI-related challenges moments to AI safety, organizations can create a culture of awareness, collaboration, and continuous learning

Hindsight: Learning from the Past Hindsight in risk and compliance involves thoroughly examining historical This retrospective analysis is where machine learning (ML) truly shines. This is where the collaboration between human expertise and machine learning is most evident. While machine learning can contribute through predictive analytics, using historical and current data Hindsight allows us to learn from past experiences, turning historical data into valuable lessons.

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

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