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False Positive Analysis

Investigating why a detection fired on benign activity, in detection engineering, so the rule can be tuned to reduce noise without losing its ability to catch real threats. It turns false positives into targeted improvements.

When a detection generates false positives, an engineer analyzes them to understand the benign behavior triggering the alert and decide how to refine the rule, tightening logic, adding context, or excluding a known-good case, while preserving true-positive coverage. Systematic false-positive analysis is core to detection quality: it keeps detections trustworthy and reduces analyst fatigue, and it feeds tuning decisions across the detection lifecycle.

Introduced in: Detection Engineering

Examples

  • Investigating a noisy rule to find the benign activity triggering it.
  • Tuning a detection's logic to cut false positives without losing real catches.
  • Adding a documented exclusion for a confirmed benign case.

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