User & Entity Behavior Analytics

Leverage machine learning to immediately detect
dangerous activity and automate response


Detect Compromised Credentials, Lateral Movement, and Other Malicious Behavior

The EventTracker actionable security intelligence platform uses Machine Learning to understand and predict normal system activities and event occurrences within an enterprise. In the context of cybersecurity, Machine Learning is leveraged for User and Entity Behavior Analytics (UEBA).

EventTracker's UEBA capabilities use machine learning to gain an understanding of how users (humans) and entities (machines) typically behave within an environment, then to find risky, anomalous activity that deviates from their normal behavior, and then to alert accordingly to what may indicate a threat. Common examples include a user accessing a system at an unusual time or location; or simply accessing a system not in their routine. In terms of entity behavior, an example would be a compromised computer being used as an entry point to attempt to log into various other servers and assets.

All of this analysis, correlation, and reporting is done by collecting and storing event and log data within the SIEM (Security Information and Event Management) technology.

EventTracker leverages machine learning capabilities to trigger detailed, actionable alerts when any abnormal user or entity behavior occurs. 

Behavior

Automate and Orchestrate Your Threat Response

Malicious insiders, privilege abuse, compromised accounts, and data exfiltration activities require continual monitoring, rule correlation, machine learning, and a dedicated SOC adhering diligently to best practices and procedures. EventTracker is the only leading SIEM platform that incorporates UEBA, response automation and orchestration, and offers companies an intelligence-driven SOC to support your threat lifecycle and compliance management efforts.


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