Episode 70 — Evaluate Machine Learning in Monitoring: Benefits, Limits, and Data Requirements
This episode explains how machine learning can support monitoring when applied with clear goals, quality data, and disciplined validation, reflecting exam expectations around modern monitoring approaches and realistic limitations. You will learn what ML-based monitoring typically does, such as anomaly detection, prioritization assistance, and pattern discovery across large event streams, and why outputs must be treated as signals requiring verification rather than definitive truth. We cover data requirements like consistent telemetry, sufficient volume, stable labeling where applicable, and feedback loops that improve models over time, plus common limits such as bias, concept drift, and environment changes that degrade accuracy. A scenario explores an anomaly spike that could indicate compromise or could be a business change, showing how to test hypotheses with additional context and avoid disruptive overreaction. Troubleshooting considerations include poor data hygiene, lack of ground truth, overreliance on vendor claims, and missing performance monitoring, emphasizing that ML is most useful when combined with rules and human judgment. 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.
