AI Governance lifecycle
From intake to retirement: governance embedded into the rhythm of business.
AI Governance must be part of strategy, capital allocation, portfolio management, product lifecycle, software delivery, data management, cyber defense, risk appetite, procurement, third-party management, incident response, audit, and performance measurement.
1. IntakeCapture purpose, business owner, users, stakeholders, geography, data, model type, vendor dependency, autonomy level, and decision impact.
2. ClassifyTier by criticality, customer impact, rights impact, safety exposure, cyber risk, sensitive data, reversibility of harm, and regulatory scope.
3. ValidateTest accuracy, fairness, robustness, explainability, privacy leakage, security abuse, hallucination, toxicity, prompt injection, drift, and failure modes.
4. AuthorizeApprove, conditionally approve, defer, reject, or escalate based on evidence, residual risk, control readiness, business value, and executive sign-off.
5. DeployRelease only with inventory record, risk tier, control set, monitoring obligations, support owner, rollback path, and shutdown owner.
6. MonitorTrack performance, drift, bias, output quality, misuse, abuse, security anomalies, privacy events, appeals, complaints, and human overrides.
7. RespondClassify incidents, contain harm, execute root cause analysis, notify stakeholders, remediate controls, update evidence, and validate closure.
8. RetireDefine retirement triggers for value decay, risk escalation, drift, vendor change, regulatory change, control failure, or redundancy.
