Artificial Intelligence & Machine Learning , Governance & Risk Management , Insider Threat

Detecting Insider Threats Through Machine Learning

Detecting Insider Threats Through Machine Learning

For years, financial services and other industries have experimented with first-generation user behavior analytics solutions. Now the security sector is starting to deploy second-generation solutions that have the promise of enhancing organizations' ability to spot and stop intrusions.

Register for this session to hear about game-changing innovations in user behavior analytics and prioritized threat response. Presenter Kurt Stammberger of Fortscale will help you:

  • Understand why rule-based user behavior analytics are a "trough of despair"
  • Learn how true machine learning can give you more accurate insights and fewer false positives
  • See how it models your users and systems autonomously, on-the-fly


The "artificial intelligence" in first-gen UBA systems required long, painful installation and machine-training projects. It was largely left to the your SOC team to define and implement the massive static rule-sets required to spot insider threats - and even then, these systems constantly sound false alarms. But the game is now changing in the UEBA space. Hear about innovations that have no rules to set up and systems that get smarter the second you turn them on. You'll see how to spot anomalous behavior quickly and accurately.

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