User and Entity Behavior Analytics (UEBA)

The User is The New Perimeter. Go Beyond Traditional Methods to Detect Threats.
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BUSINESS CHALLENGE

Threats are a moving target. Determined and persistent threat actors purposely stretch out their activity across weeks or even months, especially when most SIEM and XDR solutions are incapable of piecing together events across time. Even worse, is that these solutions primarily 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.

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CRITICAL CAPABILITIES

Gurucul UEBA detects and responds quickly to threats based on an understanding of normal activity that continuously learns and adjusts to characterize suspicious and anomalous activity. Combined with our out-of-the-box threat content and other analytical capabilities, Gurucul UEBA can help security teams quickly distinguish malicious activity from false positives.

  • OOTB Threat Content and Trained Machine Learning Models
    Detect threats immediately upon deployment with 1500+ behavior-based ML models for the most popular use cases and industries that adapt to your organization
  • Behavior Based Risk Scoring
    Our Enterprise-class risk engine combines all of our telemetry, analytics and behavioral modeling into a unified risk core that helps security teams prioritize investigation and response actions
  • Incident Response & Management
    Leverage a comprehensive case management capability allowing users to track incidents.
  • Data Masking
    Mask any data attribute using roles or individual users to support data privacy requirements.
  • Intelligent Threat Hunting
    Use multiple threat hunting methodologies including hypothesis-driven investigation, known indicators of compromise, and advanced analytics / ML investigations.
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KEY BENEFITS

  • Reduce false positives.
  • Eliminate alert fatigue.
  • Enable teams to prioritize alerts.
  • Make it possible for your security experts to focus on the most credible, high-risk alerts.
  • Track anomalous user behavior not only within your network, but also within your cloud services, machines, mobile devices, and IoT assets.
  • Determine both negligent and malicious insider risks and threats.
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WHY GURUCUL?

Using big data, Gurucul provides risk-based behavior analytics delivering actionable intelligence for security teams with low false positives.

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.

Gurucul UEBA 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.

Find threats – unknown unknowns – quickly with no manual threat hunting and no configuration. Get immediate results without writing queries, rules, or signatures.

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Top Use Cases

External, Internal, Cloud Incident Collection and Monitoring

Insider Risk and Threat Monitoring

Identify high-risk profiles with risk-based analytics, data mining, anomaly, and behavior detection. Help security teams by creating a baseline using profiling attributes from HR records, events, access repository, log management solutions and more.

Insider Risk and Threat Monitoring

Anomalous Activity Monitoring

Detect attacks using ML algorithms tuned to inspect various parameters like timestamp, location, IP address, device, transaction patterns, high-risk event codes and network packets. Identify any deviation from the normal behavior that may be indicative of a threat.

Host and Device Compromise Detection

Host / Device Compromise Detection

Detect advanced persistent threat (APT) attacks and attack vectors and predict data exfiltration by performing entity-centric anomaly detection. Correlate a wide range of parameters including endpoint security alerts, vulnerability scan results, risk levels of users and accounts used, targets accessed, packet level inspection of the requested payloads, and more.

Lateral Movement Detection Lateral Movement Detection

Lateral movement detection

Identify as threat actors attempt to traverse the network in search of finding better vantage points to download additional malware, communicate to external servers, and eventually find the location of sensitive data. By detecting unusual activity and suspicious access, Gurucul UEBA can detect this coming technique used by threat actors as part of an attack campaign.

Identify high-risk profiles with risk-based analytics, data mining, anomaly, and behavior detection. Help security teams by creating a baseline using profiling attributes from HR records, events, access repository, log management solutions and more.

Detect attacks using ML algorithms tuned to inspect various parameters like timestamp, location, IP address, device, transaction patterns, high-risk event codes and network packets. Identify any deviation from the normal behavior that may be indicative of a threat.

Detect advanced persistent threat (APT) attacks and attack vectors and predict data exfiltration by performing entity-centric anomaly detection. Correlate a wide range of parameters including endpoint security alerts, vulnerability scan results, risk levels of users and accounts used, targets accessed, packet level inspection of the requested payloads, and more.

Identify as threat actors attempt to traverse the network in search of finding better vantage points to download additional malware, communicate to external servers, and eventually find the location of sensitive data. By detecting unusual activity and suspicious access, Gurucul UEBA can detect this coming technique used by threat actors as part of an attack campaign.