Episode 28 — Manage retention and deletion to reduce long-term AI data exposure (Task 14)
This episode teaches Task 14 through retention and deletion discipline, because AI systems tend to accumulate prompts, outputs, logs, and derived artifacts that quietly expand exposure over time, and AAISM questions often test whether you can reduce that long-term risk with defensible rules. You’ll define what must be retained for security monitoring, incident response, audit, and regulatory requirements, and what should be minimized or deleted to reduce breach impact and privacy risk. We’ll use scenarios like storing conversation history for model improvement, retaining inference logs for investigations, and handling deletion requests when prompts include personal or confidential data, then connect those scenarios to policy, technical controls, and evidence capture. Troubleshooting covers over-retention due to vague defaults, inconsistent deletion across vendors and internal systems, and retention rules that conflict with legal holds or regulatory timelines, all of which can appear in exam-style tradeoff questions. 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.
