A pre-scale review for AI safety before expanding the scope

Before I trust a safety strategy at scale, I want to see documented risks, recurring eval coverage, named owners for mitigations, and a record of at least a few launch or scope decisions that changed because of the findings. That is what separates a safety practice from a safety posture deck.

Three evaluation axes to compare:
- clarity of the threat model
- repeatability of the evaluation process
- evidence that the findings change deployment choices

Review materials:
- Inspect documentation: inspect.aisi.org.uk/
One of the best places to see evaluation design turned into runnable workflows.
- AI RMF Playbook: airc.nist.gov/AI_RMF_Knowledge_Base/Playbook
The most useful NIST material when a team needs implementation moves, not just principles.
- Inspect source: github.com/UKGovernmentBEIS/inspect_ai
Open source evaluation framework from the UK AI Security Institute.

Save the strongest examples, scorecards, and decision memos in this folio so future teammates can see what good evaluation looked like at the time.