Episode 36 — Set AI Governance: Acceptable Use, Access Controls, and Monitoring Expectations
This episode explains how to build AI governance that is enforceable and sustainable, a concept the exam tests through leadership ability to translate risk appetite into policies, controls, and oversight mechanisms. You will learn how to define acceptable use in terms of permitted tasks and permitted data classes, assign ownership for approvals and exceptions, and implement access controls that reflect user roles and the sensitivity of both inputs and outputs. We explore monitoring expectations such as usage visibility, output auditing, anomaly detection for abuse, and documentation that supports later investigations and compliance reviews. A scenario covers a team adopting a new AI tool without review and how to bring it under governance without halting productivity, while troubleshooting guidance addresses policy ambiguity, uncontrolled growth of shadow usage, and gaps in vendor transparency around data handling and retention. The goal is a governance model that encourages safe adoption while preventing silent risk accumulation. 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.
