Episode 63 — Audit AI decommissioning: retirement criteria and data cleanup duties (Task 8)
This episode focuses on AI decommissioning, because Task 8 scenarios sometimes test whether you can manage the end of the lifecycle with the same discipline as development and deployment. You’ll learn how to define retirement criteria, such as models that no longer meet requirements, models that create unacceptable harm, systems that cannot be supported operationally, or use cases that no longer have a lawful basis or approved purpose. We’ll cover what “clean shutdown” looks like in audit terms: disabling endpoints, removing access, updating dependent systems, and ensuring monitoring does not silently continue generating risk through leftover integrations. You’ll also learn how data cleanup duties fit governance, including retention and deletion requirements for training datasets, logs, decision records, and artifacts that must be preserved for auditability while still respecting privacy constraints. By the end, you should be able to choose exam answers that emphasize defined retirement decisions, accountable owners, and evidence that data and access were handled correctly after decommissioning. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
