Episode 25 — Identify data risks across the AI life cycle: leaks and tampering (Task 14)
This episode teaches how to identify data risks across the AI life cycle, focusing on leakage and tampering threats that AAISM frequently tests through scenarios involving training data, evaluation sets, and production inputs and outputs. You will learn to map where data enters, moves, transforms, and is stored, then identify risk points such as over-permissive access, unsafe sharing, pipeline exposure, and weak integrity controls. We use examples like poisoning risks in training sources, leakage through prompts and logs, and tampering in feature pipelines to show why AI data risk is not limited to databases. Troubleshooting emphasizes how to detect early signals of data problems, such as abnormal drift, unexpected model behavior, or unexplained performance changes, and how to connect those signals to control gaps and investigation steps. 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.
