Assess whether deprecation of a legacy scorecard creates a governance gap

Situation

Deprecation of a legacy sits with exam-readiness coordinator because a near-miss where an agent emailed a customer unreviewed hit a pharma company using LLMs on trial documents. Evidence is training-data provenance questionnaire; write the AI Governance Inventory and Regulatory Fit option that extract can carry.

Decision

Exam-readiness coordinator in a pharma company using LLMs on trial documents must choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact using training-data provenance questionnaire after a near-miss where an agent emailed a customer unreviewed.

Hypotheses to test

• The population in training-data provenance questionnaire is the one a near-miss where an agent emailed a customer unreviewed named, so Policy or governance breach follows for this Inventory and Regulatory Fit file.
• The population in training-data provenance questionnaire is adjacent only to a near-miss where an agent emailed a customer unreviewed; Model defect is the honest AI Governance call.
• A pharma company using LLMs on trial documents already contained a near-miss where an agent emailed a customer unreviewed before training-data provenance questionnaire arrived; no new Inventory and Regulatory Fit path.
• Provenance on training-data provenance questionnaire after a near-miss where an agent emailed a customer unreviewed is broken; do not pick Policy or governance breach or Model defect yet.

Analysis required

• Split policy-or-governance failure from a model defect using prompts, outputs, and human edits in training-data provenance questionnaire.
• Reproduce the incident row in training-data provenance questionnaire and say whether it ever touched production data.
• Split policy-or-governance failure from a model defect using prompts, outputs, and human edits in training-data provenance questionnaire.
• For this AI Governance Inventory and Regulatory Fit file, read training-data provenance questionnaire against a near-miss where an agent emailed a customer unreviewed and write the one fact that would move deprecation of a legacy for exam-readiness coordinator.

Recommendation

Choose Policy or governance breach / Model defect / Dual failure / Hold for the missing fact on this AI Governance / Inventory and Regulatory Fit packet (training-data provenance questionnaire after a near-miss where an agent emailed a customer unreviewed). If training-data provenance questionnaire cannot force a AI Governance label under Inventory and Regulatory Fit, stop. Do not invent pages a pharma company using LLMs on trial documents does not have.

Command returns

• Bottom-line AI Governance option on deprecation of a legacy, then the evidence in training-data provenance questionnaire, then the action for exam-readiness coordinator
• Hypothesis scorecard against training-data provenance questionnaire: supported / rejected / untestable
• What changes deprecation of a legacy if a near-miss where an agent emailed a customer unreviewed is later withdrawn
• Named option among Policy or governance breach, Model defect, Dual failure and the fact that kills the others