AI Safety

Good safety work stops looking like a side spreadsheet as soon as it is tied to an actual release gate. The strongest public material in this area is useful because it connects threat models, evaluations, and deployment choices instead of treating them as separate essays.

Three signals I would keep in view:
- Safety work gets more durable when it is tied to release decisions, not a side spreadsheet.
- Red teaming matters most when findings change policy, tooling, or rollout gates.
- The highest-value evaluations usually combine misuse risk with normal product tasks.

Read first:
- NIST AI Risk Management Framework: nist.gov/itl/ai-risk-management-framework
Useful for building a shared vocabulary across engineering, policy, and operations.
- Anthropic research archive: anthropic.com/research
A strong public record of how a frontier lab discusses evaluations, misuse, and controls.

Documents worth saving:
- 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.
- NIST Generative AI Profile: airc.nist.gov/AI_RMF_Knowledge_Base/AI_RMF_Ge...
Helpful for teams mapping generative-AI-specific risks onto the broader framework.

Watch next:
- Anthropic video archive: youtube.com/@AnthropicAI/videos
Talks and interviews that help connect research language to deployment reality.

If this post is useful, the next contribution should add a real example, a worked document, or a failure case someone else can learn from.