Detecting Attacks with
Machine Learning

Cut Through the Noise and Static with ML and AI

Noise is the enemy of detection and response. Alert static can be overpowering, creating critical gaps in your security.

After data breaches occur, forensic investigators often find warning signs left behind. However, these are usually buried among thousands of other security alerts, including countless false positives.

Machine learning and behavioral analytics give security teams the edge they need to reduce noise and accurately pinpoint attacks. Machine learning models can isolate real attacks by classifying devices and comparing current activity to both past and peer behaviors. Unlike traditional rules, these models dynamically adjust to ignore unusual but benign activity, drastically reducing false positives.

Join our on-demand info session to find out how to maximize your security.

 You’ll learn about:

  • Real-world examples of machine learning models used to detect attacks
  • Key shortcomings of today’s machine learning approaches
  • Security best practices and the right tools used to overcome common weaknesses

See it now!

Join the info session