This is the second half of the same 1.7.x transition. In the previous post, I wrote about calibration governance: how STEM BIO-AI keeps score authority from drifting when users simulate policy posture. That was about how the system decides. This post is about a different layer: how the system speaks about risk. A local repository scanner can become trapped inside its own vocabulary. It can detect dependency issues, weak provenance language, shallow validation, reproducibility gaps, and risky exception handling. But if every finding stays only inside the scanner's internal language, the report may remain too narrow. That is the problem AIRI helped address in STEM BIO-AI 1.7.x . In this context, AIRI is used as a local risk-vocabulary layer built from the MIT AI Risk Repository ecosystem. The point is not to replace deterministic repository scanning with an external risk database. The point is to give local findings a broader risk vocabulary without turning that vocabulary into a truth claim.…