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.
“We’re thrilled about the addition of machine learning to the Threat Stack Cloud Security Platform, Oversight and Insight,” said Anthony Moisant, CIO, Glassdoor. “Threat Stack’s combination of platform and services has always provided us with extremely detailed security telemetry and actionable recommendations on risk mitigation. Applying anomaly detection on top of that will be a huge benefit to our security team as we continue to evolve and grow our cloud infrastructure.”
“Machine learning is often promoted as a silver bullet solution to all problems,” said Brian Ahern, CEO, Threat Stack. “With the introduction of ThreatML we are combining the industry’s best security telemetry, rules engine, human expertise, and now machine learning to create a truly powerful cloud security solution capable of detecting known and unknown risks. This provides our customers with better security coverage, unparalleled contextual findings, and cost benefits by reducing mean time to know and respond to threats.”