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Someone will try to sell your organisation AI governance that looks impressive - an ethics framework, a principles workshop, a thousand-question checklist. Most of it won't actually manage anything. This primer is about another view: not AI governance principles, but the underlying first principles of AI risk management.
Part one is the AI. One idea - a line through points - scaled up from machine learning to deep learning, language models, RAG systems, and agents. Every step up buys more capability and more of the three U's: uncertainty, unexpectedness, unexplainability. No equations, no code - just the mental models, including why language models make things up, why testing once tells you little, and why the sensible architecture is often the boring one.
Part two is the risk management. The whole framework fits on a napkin: what do you have, how are you managing it, who's accountable. Then one level down - the system that answers those questions as a cycle, the lifecycle controls in 3 groups, and why the controls only work together, as a system, not a checklist.
Written through the financial sector's risk lens by the author of Singapore's AI risk management guidelines, but built to work for hospitals, hiring, and government services too. About 36 pages.
This primer is the gateway to the full book and guided course - AI Risk Management from First Principles - planned for 4Q 2026.
Someone will try to sell your organisation AI governance that looks impressive - an ethics framework, a principles workshop, a thousand-question checklist. Most of it won't actually manage anything. This primer is about another view: not AI governance principles, but the underlying first principles of AI risk management.
Part one is the AI. One idea - a line through points - scaled up from machine learning to deep learning, language models, RAG systems, and agents. Every step up buys more capability and more of the three U's: uncertainty, unexpectedness, unexplainability. No equations, no code - just the mental models, including why language models make things up, why testing once tells you little, and why the sensible architecture is often the boring one.
Part two is the risk management. The whole framework fits on a napkin: what do you have, how are you managing it, who's accountable. Then one level down - the system that answers those questions as a cycle, the lifecycle controls in 3 groups, and why the controls only work together, as a system, not a checklist.
Written through the financial sector's risk lens by the author of Singapore's AI risk management guidelines, but built to work for hospitals, hiring, and government services too. About 36 pages.
This primer is the gateway to the full book and guided course - AI Risk Management from First Principles - planned for 4Q 2026.