The important question is not simply whether an alert led to an incident; it is whether the detector’s stated claim was wrong. A scanner reporting a vulnerability that does not exist and an analytic classifying ordinary activity as credential theft are false positives.
An alert can also be accurate without representing hostile activity. For example, a rule may correctly detect attack-like commands during an authorized penetration test. Many teams call this a benign positive: the observed behavior and detection logic were real, but the context made the activity permitted or harmless. Disposition labels vary among organizations, so each label should have a documented meaning.
Key points
Classification testState what the detector claims, verify the underlying evidence, then separately assess authorization, intent, impact, and response need.
Common causesBroad thresholds, missing context, inaccurate asset or identity data, parsing errors, environmental changes, and assumptions that no longer hold.
Useful feedbackRecord why the finding was closed and feed recurring patterns into rule tuning, enrichment, allowlist governance, or upstream data fixes.
Important limitationA low false-positive rate does not prove that detection is effective. A rule can stay quiet because it is too narrow, receives incomplete telemetry or misses the activity entirely.