Threat Stack announced ThreatML, its new machine learning engine that enhances security observability for the Threat Stack Cloud Security Platform, Threat Stack Oversight, and Threat Stack Insight with anomaly detection.
The Threat Stack Cloud Security Platform collects, normalizes, and analyzes over 60 billion events per day from customer cloud infrastructure and applications. ThreatML leverages this rich telemetry to train its machine learning models, which are then used to detect anomalous behavior. Together, Threat Stack’s rules engine and advanced machine learning capabilities allow customers to quickly detect, prioritize, and respond to both known and unknown threats.
Threat Stack’s approach to cloud security is unique. By combining full-stack telemetry, machine learning, rules, and human expertise, Threat Stack empowers security teams to accelerate mean-time-to-know (MTTK), focus on high-severity threats, save time, and reduce cost.
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