Episode 73 — Audit access to model artifacts, pipelines, and configuration repositories (Task 14)
This episode focuses on auditing access controls for model artifacts, pipelines, and configuration repositories, because Task 14 expects you to protect the elements that directly shape AI outcomes and evidence integrity. You’ll learn how to evaluate who can view, modify, approve, and deploy model versions, datasets, feature logic, and configuration baselines, and why “developer convenience” is not a valid reason for broad, unmanaged access. We’ll cover practical access control expectations such as least privilege, separation of duties where risk justifies it, strong authentication, audit logging, and documented approvals for privileged changes. You’ll also learn how to test whether access controls are operating, including reviewing role assignments, sampling change events for proper approvals, validating logging completeness, and checking whether service accounts and automation are governed with the same rigor as humans. By the end, you should be able to answer exam scenarios by selecting the approach that preserves integrity, accountability, and traceability across the AI build and release pipeline. 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.
