Detection coverage that improves while you sleep
AI agents that continuously author, validate, backtest, and deploy detection rules across endpoint, network, cloud, and identity. Not just monitoring rules — actually writing new ones.
The Detection Crisis
Your security tools are blind
Detection engineering is broken. The numbers tell the story.
21%
detection coverage despite 90%+ telemetry available
18%
of deployed SIEM rules are silently broken
83%
false positive rate — 4,484 alerts/day average
67%
of alerts go completely unaddressed
Source: CardinalOps State of SIEM Detection Risk 2025
How It Works
The Detection Lifecycle
Five stages, fully automated, with humans in the loop where it matters.
Threat Intel Ingestion
AI agents continuously ingest MITRE ATT&CK, CISA KEV, vendor advisories, OSINT feeds
Rule Generation
Specialized agents author detection rules in Sigma/YARA/native formats
Multi-Agent Validation
Adversarial agents try to evade. Coverage agents check for gaps. Consensus required.
Human Review Gate
Analyst sees rule + context + validation results. One-click approve.
Deploy & Monitor
Detection-as-code deployment. Continuous monitoring. Auto-rollback if quality drops.
Network Effects
The Flywheel
More customers
Broader deployment across orgs
More detection signal
Richer behavioral data at scale
Better AI models
Models trained on real-world attacks
Stronger rules
Higher precision, lower false positives
Lower costs
Efficiency gains compound
Every detection deployed makes the next one sharper.
Detection rules that write themselves
See how AI agents write detection rules for your environment.