Platform · Detection
Detections written for your environment, not for everyone's.
Agentic attacks are not predictable. They move at machine speed, probe at near-zero marginal cost, and vary their behavior across identity, cloud, endpoint and SaaS. Static rules written before the attack existed cannot answer that. Artemis detects from a living model of your environment, and automatically adapts as both the environment and the threat landscape change.
Who this is for
SOC leads and detection engineering teams carrying more alerts than analysts, and more rules than anyone has time to maintain.
Adaptive detection
Artemis continuously synthesizes detectors against your specific environment, scored for quality and proposed for approval, as well as a vast amount of threat intelligence. They then tune themselves continuously, so coverage does not decay the moment someone stops maintaining it.
Behavioral analytics
Anomaly detection grounded in the model of what is expected for each entity and asset in your organization rather than what is average across everyone's. A data transfer that is routine for one service account and unprecedented for another is only detectable when the model sits in the detection path.
Core detection library
Comprehensive MITRE ATT&CK coverage maintained by the Artemis research team and validated against real environments. Live on day one, and the foundation the other two layers build on. Import your existing Sigma or SIEM rules alongside it within minutes.
"The detections aren't generic. They're tuned to what's actually happening in our infrastructure. When the system flags something, my team trusts it."
Branden Wagner, Head of Security. 
Frequently asked questions
Yes. Import existing Sigma or SIEM rules, or author detectors in plain language through AI Mode, with versioning, audit history and one-click rollback.
Detectors tune themselves and self-heal. That is why false positives fall over a contract term rather than accumulating.
Every detector is validated against your historical data and scored for volume, overlap, accuracy and efficacy before deployment.