Episode 30 — Define AI security metrics leaders can understand and act on (Task 18)
This episode teaches how to define AI security metrics that drive decisions, because AAISM scenarios often test whether you can choose measurements that are meaningful to executives and useful to operators. You will learn to distinguish activity metrics from outcome metrics, and to build a small set that reflects risk reduction, control performance, and exposure trends, such as inventory coverage, high-risk model counts, access exceptions, drift events tied to security triggers, and incident response time to contain. We use examples of poorly designed metrics, like counting policies written or training hours completed, to show why they fail to predict risk and do not motivate action. Troubleshooting focuses on setting thresholds, assigning metric owners, validating data quality, and ensuring reporting leads to prioritization rather than noise. 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.
