Gurucul UEBA

Gurucul UEBA is a User and Entity Behavior Analytics (UEBA) security solution designed to detect and mitigate threats in enterprises of various sizes. According to the vendor, this platform caters to security teams, IT professionals, risk management professionals, and compliance officers across industries such as financial services, healthcare, federal, retail, energy & utilities, and manufacturing.

Key Features

Out-of-the-box Threat Content and Trained Machine Learning Models: According to the vendor, Gurucul UEBA provides behavior-based machine learning (ML) models for immediate threat detection upon deployment. These ML models are trained to adapt to the organization's specific needs and continuously learn and adjust to characterize suspicious and anomalous activity.

Behavior Based Risk Scoring: Gurucul UEBA features an enterprise-class risk engine that generates a unified risk score per user and entity. This enables security teams to prioritize investigation and response actions, focusing on the most credible and high-risk alerts to potentially reduce false positives and alert fatigue.

Incident Response & Management: Gurucul UEBA includes comprehensive case management capabilities, allowing users to track and manage security incidents efficiently. This feature is intended to enhance the incident handling process, ensuring timely resolution and mitigation.

Data Masking: Gurucul UEBA offers data masking capabilities to support data privacy requirements. According to the vendor, users can mask any data attribute using roles or individual user settings, safeguarding sensitive information and complying with data privacy regulations.

Intelligent Threat Hunting: Gurucul UEBA provides multiple threat hunting methodologies, allowing security teams to proactively search for potential threats. This capability is said to enhance the organization's ability to detect and respond to emerging threats that may go undetected by traditional security measures.

Reduce false positives: According to the vendor, Gurucul UEBA helps organizations reduce the number of false positive alerts generated by security systems. By utilizing advanced analytics and machine learning algorithms, the solution aims to accurately identify and filter out non-threatening events, potentially minimizing alert fatigue for security teams.

Eliminate alert fatigue: The advanced analytics and machine learning capabilities of Gurucul UEBA are designed to prioritize and consolidate alerts based on their risk level. By potentially eliminating redundant and low-risk alerts, security teams can focus their attention on the most critical and high-priority threats, potentially reducing alert fatigue.

Enable teams to prioritize alerts: Gurucul UEBA provides a unified risk score per user and entity, enabling security teams to prioritize alerts based on their potential impact and severity. This allows efficient resource allocation and addressing of the most significant threats first, according to the vendor.

Track anomalous user activity with behavior analytics: Gurucul UEBA leverages behavior analytics to track and monitor anomalous user activity across various assets, including the network, cloud services, machines, mobile devices, and IoT assets. By analyzing behavior patterns, the solution aims to detect both negligent and malicious insider risks and threats, according to the vendor.