Episode 34 — Tune noisy detections using feedback loops that shrink backlogs over time
This episode teaches detection tuning as an iterative feedback loop that improves signal quality while preserving coverage, which GSOM tests because “turn it off” is rarely the right long-term answer. You will define noise sources such as overly broad logic, missing allowlists for known-good behavior, poor asset or user context, and environmental changes like new software deployments that shift baselines. We will connect tuning to backlog reduction by showing how to prioritize which detections to refine first, using metrics like alert volume, time-to-triage, false positive rate, and the business cost of analyst distraction. Real-world scenarios include an alert that fires on legitimate administrative tools, correlation rules that duplicate EDR detections, and cloud audit events that explode after a policy change, with best practices for staged changes, validation periods, and rollback plans so tuning does not accidentally create blind spots. 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.
