Generative AI can produce fluent but unsupported statements, omit important context, expose sensitive details, or treat populations inconsistently. Map these possibilities to the NIST trustworthiness characteristics: validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed.

For this course, human review is a control, not a ceremonial sign-off. The reviewer needs enough context, authority, time, training, and evidence to correct or reject the output. A generated summary must not silently become a credit, eligibility, pricing, underwriting, investment, claims, or suspicious-activity decision.

When a failure is found, preserve the relevant evidence, contain the workflow, notify the accountable owner, and route the issue to the right compliance, legal, privacy, security, risk, conduct, or business reviewer. Determine who may have been affected and what communication is required under organizational policy, contract, and applicable law. Then document root cause, corrective action, monitoring change, and the decision to resume, restrict, replace, or retire the use case.

Incident disclosure is not the same as publishing sensitive details. It means communicating responsibly to the people, functions, customers, partners, or authorities who need accurate information, using the approved escalation path.