Episode 23 — Evaluate AI in OT Security: ML, Generative AI, and Operational Risk Tradeoffs
This episode explains how to evaluate AI claims in OT security without falling into hype or blanket rejection, because the exam and the real world both reward balanced judgment grounded in operations. You’ll distinguish machine learning used for anomaly detection and prediction from generative AI used for summarization, automation, and decision support, then connect each to the data quality constraints common in OT such as sparse logs, proprietary protocols, and changing baselines during maintenance. We discuss where AI can help, like identifying subtle deviations, triaging alerts, and accelerating documentation, and where it can hurt, like amplifying false positives, masking root causes, or encouraging actions that are unsafe when applied to control environments. You’ll learn evaluation criteria that map cleanly to exam scenarios, including explainability, validation against known-good baselines, handling of drift, and the need for human-in-the-loop authority when physical processes are at stake. The episode closes by teaching safe adoption patterns such as pilot programs, gated automation, and clear accountability so AI improves resilience without becoming a fragile dependency. 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.
