A genuinely useful starter pack for AI safety

A usable safety starter pack should have one framework, one research archive, one evaluation tool, and one red-teaming toolkit. That mix gives people language, examples, executable tests, and a reminder that adversarial work needs its own craft, not just more benchmark rows.

The kinds of materials worth saving in this space:
- governance frameworks with concrete implementation guidance
- evaluation reports that describe methods and limitations
- incident retrospectives that explain organizational response

Read:
- 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.
- Inspect documentation: inspect.aisi.org.uk/
One of the best places to see evaluation design turned into runnable workflows.

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

Build or inspect:
- Inspect source: github.com/UKGovernmentBEIS/inspect_ai
Open source evaluation framework from the UK AI Security Institute.
- PyRIT: github.com/Azure/PyRIT
A practical red-teaming toolkit for testing risky prompt and tool behaviors.

Image references:
- AI RMF knowledge base: airc.nist.gov/AI_RMF_Knowledge_Base/
Framework visuals and navigable references that are easier to browse than a single PDF.