Decommissioning & Lifecycle Closure

Every AI system eventually reaches the end of its operational lifecycle. Whether due to changing business requirements, declining performance, regulatory obligations, technology modernization, or risk considerations, organizations must retire AI systems in a controlled and accountable manner. This lesson explores decommissioning and lifecycle closure, examining retirement planning, evidence retention, archival requirements, data disposition, access revocation, compliance obligations, and lessons-learned processes. Learners will study how organizations preserve accountability after systems are retired while ensuring that governance, auditability, and regulatory requirements continue to be satisfied. Understanding lifecycle closure is essential for evaluating complete AI governance programs.