The quiet mistakes that slow people down in AI safety

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.

Common traps to watch:
- treating policy text as a substitute for operational controls
- testing only polished prompts instead of adversarial or low-context inputs
- reporting scores without showing what changed because of them

References that help correct the drift:
- Anthropic research archive: anthropic.com/research
A strong public record of how a frontier lab discusses evaluations, misuse, and controls.
- 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.

This folio post is meant to be saved and revised. Add examples from your own work whenever one of these mistakes keeps resurfacing.