Reading and frameworks for teams evaluating and governing AI in production.
A score does not prove control. Boards, security teams and regulators need to reconstruct what was tested, against which version, with which traces, which judge, which evidence and which residual risk decision.
When a board asks about AI risk, it is asking more than whether the model scores well. It wants to know what the system can damage, which controls exist, and what evidence shows those controls still work.
Stanford counted 362 documented AI incidents in 2025, and the OECD Incidents Monitor recorded a peak of 435 in January 2026. AI can leave the pilot phase, but production teams need evidence that it still works after every model, data, instruction, tool or policy change.
If an AI tool helps approve credit, detect fraud, screen transactions or answer customers, it is no longer just a model governance topic. Under DORA, it may also be an ICT asset, a third-party dependency and an incident source.
A chatbot can invent a policy or write a bad answer. An agent can delete data, send emails, place orders or deploy code. That difference changes how security and governance teams should think about risk.