Certified: The ISACA AAIA Audio Course

This episode focuses on preserving data integrity so models remain reliable, which is central to Task 14 because AAISM treats integrity failures as both a security problem and a governance problem when decisions depend on model outputs. You’ll define integrity controls such as dataset versioning, provenance tracking, validation checks, change approvals, and monitoring signals that detect unexpected shifts in data distributions or labeling quality. We’ll work through a scenario where a model’s output quality degrades after a pipeline change, and you’ll practice tracing the issue back to data integrity causes like corrupted records, unauthorized modifications, or subtle poisoning introduced through third-party feeds. Best practices include separating trusted from untrusted sources, using controlled promotion from development to production datasets, and documenting integrity checks so reviewers can verify that training and evaluation were performed on known-good data. On the exam, you’ll learn to favor answers that create repeatable integrity assurance over answers that only re-train and hope. 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.