Certified: The ISACA AAIA Audio Course

This episode explains why change management for AI must be stricter than typical software change management, because in AI, “updates” can silently change outcomes even when interfaces stay the same. You’ll learn how changes can enter through code, data sources, feature logic, model parameters, infrastructure dependencies, and even operating conditions, and why each path needs control, testing, and documentation. We’ll cover what strong AI change management looks like: defined change categories, required approvals, validation requirements proportional to risk, and clear communication to stakeholders when decision behavior changes. You’ll also learn the evidence auditors expect, including change tickets tied to risk assessments, test results, approvals, version histories, and post-change monitoring plans. By the end, you should be able to answer AAIA questions by selecting the option that treats AI changes as outcome-changing events with measurable controls, not as routine patches pushed on a schedule. 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.