Assess whether vendor terms allow customer data in training (c1747e)

SITUATION The working file is output-scoring rubric that never fails a high-risk output after a purchase order signed before eval data rights were granted. Model-deprecation manager in a hospital committee that never records dissent has to name Policy or governance breach or Model defect for this AI Governance Layer Control Plane and Scoring file.

DECISION Model-deprecation manager in a hospital committee that never records dissent must choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact using output-scoring rubric that never fails a high-risk output after a purchase order signed before eval data rights were granted.

HYPOTHESES TO TEST 1. A purchase order signed before eval data rights were granted is noise around an already-controlled Control Plane and Scoring process in a hospital committee that never records dissent, given output-scoring rubric that never fails a high-risk output. 2. A purchase order signed before eval data rights were granted is the event in output-scoring rubric that never fails a high-risk output that forces Policy or governance breach for model-deprecation manager under AI Governance Layer. 3. Output-scoring rubric that never fails a high-risk output shows a one-file miss after a purchase order signed before eval data rights were granted, not a Control Plane and Scoring program failure. 4. Output-scoring rubric that never fails a high-risk output cannot decide vendor terms allow customer yet after a purchase order signed before eval data rights were granted; hold is the only AI Governance Layer close a hospital committee that never records dissent can defend.

ANALYSIS REQUIRED 1. Name the override that would let vendor terms allow customer proceed without a silent bypass. 2. Test whether a purchase order signed before eval data rights were granted changed routing, logging, or human-in-the-loop on the live agent path. 3. Score whether the agent action in output-scoring rubric that never fails a high-risk output was in-policy, out-of-policy, or unlogged. 4. For this AI Governance Layer Control Plane and Scoring file, read output-scoring rubric that never fails a high-risk output against a purchase order signed before eval data rights were granted and write the one fact that would move vendor terms allow customer for model-deprecation manager.

RECOMMENDATION Choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact on this AI Governance Layer / Control Plane and Scoring packet (output-scoring rubric that never fails a high-risk output after a purchase order signed before eval data rights were granted). The follow-on Control Plane and Scoring action is what model-deprecation manager does next: implement the option, assign an owner, and log the missing fact.