Use Case: SOC Operations
False positive reduction
Your SIEM fires 1,200 alerts per day. Your team of 4 analysts can meaningfully investigate 80. ManySignal triages 1,050 automatically, presents 150 to analysts, and 120 of those are real threats.
Alert fatigue is a detection failure
When analysts are overwhelmed by alerts, they triage faster and less carefully. They dismiss alerts based on superficial inspection rather than full investigation. They develop pattern recognition for "probably nothing" that occasionally misses a real threat disguised as a familiar pattern. Alert fatigue isn't just a morale problem — it's a detection gap that attackers knowingly exploit. Campaigns that trigger high alert volumes simultaneously with their real attack vector use noise as a smokescreen.
False positive reduction is the foundational capability that makes everything else in the SOC work. A SOC where analysts trust their alerts, because the noise has been systematically removed, is a fundamentally different operational environment from one where alerts are treated as guilty until proven innocent.
73%
Reduction in analyst-reviewed FPs
average across customer deployments
90s
Time to triage (automated)
vs 28 min manual average
99.2%
True positive preservation
no real threats suppressed
approach: multi-layer_false_positive_reduction
layer_1_automated_triage:
- run: full_investigation_for_every_alert
- if: all_evidence_negative → auto_close with rationale
- if: mixed_evidence → score and queue for analyst
layer_2_suppression_learning:
- track: analyst_close_actions (false_positive label)
- pattern: extract_context_signature from FP alerts
- suppress: future_alerts matching signature AND context
- threshold: require 5 analyst FP closes before auto-suppress
layer_3_context_enrichment:
- goal: give analyst enough context to decide in 30 seconds
- include: entity_history, behavioral_deviation_score, related_alerts
- result: analyst confident decisions, fewer close-as-FP
layer_4_continuous_tuning:
- report: weekly FP rate by rule and source
- recommend: threshold adjustments, suppression additions
- track: true_positive_rate alongside FP_rateFalse positive reduction FAQ
What false positive rate can organizations expect after deploying ManySignal?
Customer data shows an average 73% reduction in analyst-reviewed false positives within 60 days of deployment. The reduction comes from three sources: automated triage that closes obvious false positives without analyst review, behavioral context that disambiguates genuinely anomalous events from noisy rules, and suppression learning that identifies recurring benign patterns and auto-suppresses them.
How does ManySignal learn which alerts are false positives without analyst feedback?
ManySignal uses several signals: analyst close actions (closing an alert as false positive teaches the model), enrichment outcomes (if the triage agent's investigation finds no supporting evidence across all data sources, it scores the alert as low confidence), and cross-customer learning (with customer consent, anonymized false positive patterns from similar environments inform suppression rules).
Can ManySignal auto-close alerts that are confirmed false positives?
Yes. Alerts where all triage agent investigations return negative results, and where the alert type and context match a known-benign pattern, are auto-closed with a documented rationale. The auto-close rate and rationale are reported weekly, and analysts can review and reverse any auto-close within 30 days.
Does false positive reduction affect the detection of real threats?
The suppression rules are tuned to reduce noise without reducing true positive coverage. ManySignal tracks the true positive rate alongside the false positive rate. Suppression rules that would have suppressed a confirmed true positive are flagged and reviewed. In practice, well-tuned suppression rules improve true positive rate by reducing alert fatigue — analysts investigate more carefully when the queue is shorter.
Cut your alert queue by 73% without cutting coverage
Automated triage, suppression learning, and context enrichment — working together from day one.