Episode 98 — Collect AI audit evidence: logs, lineage, artifacts, and change records (Domain 3C)
This episode explains how to collect AI audit evidence across logs, lineage, artifacts, and change records, because Domain 3C expects you to prove what happened, when it happened, and under which model and data conditions. You’ll learn how operational logs support questions about access, inference usage, exceptions, and incidents, while lineage artifacts support questions about where data came from, how it changed, and how it was used in training and validation. We’ll cover model and pipeline artifacts such as version histories, configuration baselines, validation results, and release packages that tie behavior to controlled approvals. Change records will be treated as the backbone of accountability, linking updates to risk assessments, test evidence, approvals, and post-change monitoring. You’ll also learn how to avoid evidence traps, such as collecting documentation that is not tied to the current release, or accepting screenshots and summaries without underlying records. By the end, you should be able to choose exam answers that prioritize evidence that is traceable, repeatable, and linked to specific AI behavior in production. 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.
