AI Safety, Security, and Governance
Governance, Traceability, and Responsible AI
Create auditable governance for data sources, model and prompt versions, approvals, evaluations, human oversight, bias, explainability, policy compliance, and continuous monitoring.
Concepts
- Trace an output to source documents, model, prompt, guardrail, configuration, code version, and evaluation evidence.
- Define accountable owners, risk tiers, approval gates, exception processes, and retirement criteria.
- Measure responsible AI properties against the intended population and use case, not as one universal score.
- CloudTrail audit events and application decision logs answer different questions and are both needed.
Exam tips
- CloudTrail records supported AWS API activity; CloudWatch Logs stores application and model interaction logs.
- Model cards communicate intended use, evaluation results, limits, and risks.
- Human oversight should be proportional to impact and preserve enough context for a meaningful decision.