Episode 28 — Manage retention and deletion to reduce long-term AI data exposure (Task 14)
This episode focuses on retention and deletion as risk-reduction controls for AI data, which AAISM tests through scenarios involving compliance obligations, privacy expectations, and the operational reality that data and logs tend to accumulate. You will learn how to define retention rules for training data, evaluation data, embeddings, prompts, and inference logs based on business need, legal duties, and risk tolerance, and how to implement deletion workflows that are provable rather than assumed. We cover examples like limiting retention of sensitive prompts, rotating datasets, and handling right-to-delete requests where applicable, while ensuring governance approvals and evidence trails remain intact. Troubleshooting highlights gaps like unknown copies, vendor-held data with unclear deletion terms, and retention rules that exist on paper but are not enforced by systems or monitored for drift. 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.
