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"How do you measure false negatives when AI is generating detection logic?"
It's a fair question and one I expect we'll hear more often as AI becomes part of detection engineering.
False negatives matter, but I don't think they're the right operational KPI.By definition, false negatives represent malicious activity that wasn't detected. While organizations can estimate them through attack simulations, historical replay, purple teaming, incident retrospectives, or forensic analysis, those exercises are periodic, not continuous.
The better question is:
Is your detection program becoming more effective over time?
That's wheredetection maturitycomes in.
Instead of focusing primarily on what you may have missed, measure what you can objectively improve:
- MITRE ATT&CK coverage
- Detection gaps identified and remediated
- Historical replay and validation results
- Threat intelligence coverage
- Reduction in false positives and alert noise
- Time to create, validate, and deploy new detections
- Detection freshness as the threat landscape evolves
To me, detection engineering isn't about generating SPL, KQL, or Sigma rules faster.
It's about continuously assessing detection posture, identifying coverage gaps and inefficiencies, validating improvements, and feeding investigation outcomes back into the detection lifecycle.
Detection engineering should be managed as a continuous improvement discipline, not a one-time rule-writing exercise.
Measure the maturity of your detection program. The outcomes will follow.
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