Threat detection that evolves with the attacker

What we build

Effective detection is behavioral, not lexical. We instrument your environment, establish a baseline of what legitimate activity looks like across identities, endpoints, network and data access, and train models that surface anomalous behavior - the lateral movement, the credential abuse, the data exfiltration pattern - before it completes.

• Behavioral baselines per identity, workload and data store, not generic industry templates
• Anomaly models that catch zero-day-style behavior even when the payload itself is novel
• Identity-focused detection: impossible travel, token abuse, privilege escalation, service-account drift
• Data-exfiltration patterns across egress, storage and copy/print surfaces
• Feedback loop: analyst verdicts retrain the model, so detection improves with every confirmed incident

Why your own telemetry matters

A generic anomaly model trained on someone else's network flags your legitimate maintenance as suspicious and misses your actual exfiltration because it looks normal to a different baseline. Training on your telemetry is the difference between signal and noise.

It gets sharper over time

Every confirmed true positive and every false positive feeds back into retraining. The detection model your SOC uses in month six is materially better than the one it used in month one - by design, not by hope.