Measure whether alerts lead to timely, explainable decisions. Track duplicate or repeated signals, missing asset or identity context, closure reasons, escalation to incidents, ageing, unresolved material alerts, analyst tuning feedback and source-related failures. High alert volume is not proof of strong detection, and a low false-positive rate can still hide missed coverage.
External reference
NIST continuous-monitoring guidance emphasises ongoing visibility and information needed to respond to risk in a timely manner.
https://csrc.nist.gov/pubs/sp/800/137/finalDetection engineering workflow
Measure decision quality
Retain triage reason, ownership, severity and disposition so alert outcomes can be analysed.
Measure context quality
Track missing asset, identity, threat, vulnerability or source context that slows decision-making.
Measure duplication and ageing
Identify repeated alerts, unresolved material items and signals that remain open without a clear next action.
Measure incident conversion
Understand which alert classes escalate to incidents, which close as benign and where escalation criteria are inconsistent.
Feed tuning back into engineering
Use analyst disposition, incident outcomes and source-health evidence to refine or retire detections.
Relevant Cybatar operating surfaces
Claim boundary
No single metric proves detection quality. False-positive rates, incident-conversion rates and alert counts can be misleading without coverage, severity, environment and missed-detection context.
These pages are Cybatar-authored detection-engineering guidance. External taxonomies and standards are referenced for context; the mappings are not official MITRE, NIST or vendor validations, certifications, endorsements or statements that a particular deployment will detect a technique.