SIEM False-Positive Reduction
A structured tuning program that reduced SIEM false positives by ~40% while preserving detection coverage.
Sanitized case study — lab / generalized evidence only
Problem
High false-positive volume causes alert fatigue and slows real incident response. The goal was to cut noise without creating blind spots.
Scope
- Correlation tuning, thresholding, and log filtering
- SIEM analytics review (generalized)
- No internal thresholds or environment-specific logic disclosed
Tools
SplunkWazuhElasticCorrelation rulesLog analysis
Methodology
- 1Baseline
Quantify the noisiest rules and categorize the root causes of false positives.
- 2Tune
Refine correlation, calibrate thresholds, and filter known-benign patterns with documented justification.
- 3Verify
Confirm that tuning preserves true-positive detection using replayed/simulated activity.
- 4Document
Record every change so tuning is auditable and reversible.
Result & Impact
- ~40% reduction in false positives
- Lower analyst fatigue and faster focus on real incidents
- Auditable, reversible tuning decisions
Evidence (sanitized)
- Before/after false-positive rate summary (generalized)
- Change log excerpt showing justification per tuning decision
Lessons Learned
- Tuning is risk management: always verify you did not create a blind spot.
- Document the why, not just the what.
- Noise reduction is a continuous process, not a one-off project.