Overview

Threats are a moving target. Determined and persistent threat actors purposely stretch out their activity across weeks or even months. Most solutions are incapable of piecing together events across time. Even worse, many use rule-based Machine Learning, which is essentially pattern matching. This makes them especially ineffective in detecting new attacks and/or variants, which are highly successful in breaching organizations.

Gurucul UEBA takes a different approach, detecting and responding quickly to threats based on an understanding of normal activity and continuously learns and adjusts to characterize suspicious and anomalous activity. This is combined with Gurucul’s out-of-the-box threat content, big data, and other analytical capabilities to help security teams quickly distinguish malicious activity from false positives so security teams have actionable intelligence.

Gurucul leads the market in demonstrating UEBA results where others cannot. The product consumes the most data sources out-of-the-box and leverages the largest machine learning library. It also delivers a single unified prioritized risk score per user and entity. This risk score is the key indicator used to drive down-stream automated security controls and processes.

Furthermore, Gurucul offers a variety of critical capabilities including trained ML models, incident response and management, data masking, intelligent threat hunting, the ability to create custom ML models, open choice of big data, case management and more. For organizations and security teams looking to reduce insider risk, uncover host and device compromise, identify anomalous activity, stop lateral movement across networks, and reduce false positives, Gurucul UEBA is the industry’s premier choice.