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.

The common trap is treating policy text as if it were a control. The next trap is benchmarking only polished prompts and then sounding surprised when messy real user behavior produces a very different risk profile. The workflow that seems to hold up is: define harms that matter to real users, build evals that mirror those harms, run them on a cadence, and let the findings change rollout decisions. Anything softer than that tends to produce documentation without leverage.

If you want a cleaner start, build your notes around ai-safety, red-teaming, and the real examples behind safety work gets more durable when it is tied to release decisions, not a side spreadsheet.. Those records will outlast the summary you write about them later.

Open alongside this question:
- NIST AI Risk Management Framework: nist.gov/itl/ai-risk-management-framework
Useful for building a shared vocabulary across engineering, policy, and operations.
- 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.
- Anthropic video archive: youtube.com/@AnthropicAI/videos
Talks and interviews that help connect research language to deployment reality.