Episode 64 — Audit AI Use at Scale: Who Asked What, When, and With What Data

This episode focuses on auditing AI usage as a governance and security requirement, because SecAI+ expects you to prove accountability across prompts, retrieval, tools, and outputs when the organization is challenged by incidents, regulators, or internal oversight. You will learn what “who asked what, when, and with what data” means operationally, including identity attribution, request context, the data sources that were accessed, and the specific model and prompt versions involved in producing an output. We will connect auditability to multi-tenant and enterprise environments where service accounts can hide user identity if identity is not propagated end-to-end, and where retrieval systems can leak data if access checks are not enforced at query time. You will also learn how to design audit records that support both investigations and privacy obligations, capturing necessary metadata and decision traces without storing excess content. Troubleshooting considerations include reconciling logs across distributed services, preventing gaps created by caching or asynchronous tool calls, and creating reporting that helps leaders understand usage trends and risk hotspots without turning audits into manual archaeology. 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.