Episode 48 — Detect AI and ML privacy pitfalls like inference, drift, and overcollection risks (Domain 4C-5 AI/Machine Learning (ML) Considerations)
This episode focuses on privacy pitfalls that appear after AI and ML systems go live, including inference risks, drift-driven behavior change, and overcollection through “helpful” logging and feedback loops. You’ll learn how models can reveal sensitive information through outputs, how prompt and input data can become unintended data collection, and how monitoring designed for performance can accidentally capture personal information at scale. We’ll discuss practical safeguards such as output filtering, prompt and input minimization, access controls for inference endpoints, secure handling of user feedback, and monitoring that detects abnormal query patterns or data leakage without storing unnecessary content. You’ll also troubleshoot scenarios where model updates change outcomes, where drift leads to new use of sensitive signals, or where vendors do not provide enough transparency, practicing exam-ready responses that emphasize measurable controls, clear evidence, and continuous review rather than one-time approval. 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.
