Episode 45 — Data Analytics for Detection: Baselines, Outliers, Correlation, and Meaningful Signals (Task 6)
This episode explains data analytics concepts used in detection engineering, focusing on what makes a signal meaningful and how analysts avoid being overwhelmed by noise. You will define baselines, outliers, correlation, and contextual enrichment, then learn how each concept supports stronger alert quality and faster investigation. We will discuss why baseline building must account for business cycles, system changes, and user behavior variation, and how naive thresholds can create alert storms or miss slow, stealthy activity. You will also hear practical examples of correlating endpoint events with identity logs, network flows, and application telemetry to increase confidence that an event matters. For the exam, the emphasis is selecting analytics approaches that improve detection fidelity while remaining operationally maintainable and explainable to stakeholders. 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.
