Episode 79 — Manage privacy requirements across AI inputs, outputs, and user access (Task 3)
This episode explains how to manage privacy requirements across AI inputs, outputs, and user access, with an exam focus on turning privacy expectations into enforceable controls and provable evidence. You will learn how privacy risk shows up through training data selection, user-provided prompts, inference logs, and generated outputs that may reveal sensitive information or infer protected details. We use scenarios like an internal assistant accessing regulated data and a customer-facing model handling user submissions to show how consent, minimization, purpose limitation, retention, and access controls must align across the full flow. Troubleshooting focuses on privacy failures such as logging too much, retaining too long, allowing broad user access without role-based constraints, and making transparency claims that are not supported by system behavior or evidence. 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.
