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Gurucul UEBA

Find & Stop Threats Immediately with Behavior Based Predictive Risk Scoring

Gurucul User & Entity Behavior Analytics (UEBA) uses machine learning models on open choice big data to detect unknown threats early in the kill chain. UEBA provides the most realistically effective approach to comprehensively manage and monitor user and entity centric risks. UEBA quickly identifies anomalous activity, thereby maximizing timely incident or automated risk response. The range of Gurucul UEBA use cases is what makes the solution extensible and valuable. It focuses on the detection of risks and threats beyond the capabilities of signatures, rules and patterns.

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. We consume the most data sources out-of-the-box and leverage the largest machine learning library. Additionally, we deliver a single unified prioritized risk score per user and entity. Find threats – unknown unknowns – quickly with no manual threat hunting and no configuration. Get immediate results without writing queries, rules or signatures.

Gurucul UEBA Capabilities

Gurucul Studio


Largest library of prepackaged machine learning models and the ability to build your own

Enterprise Risk Engine

Enterprise Risk Engine

Drive risk-based security controls and define risk your way

Alerting and Case Management

Alerting & Case Management

Comprehensive case management capabilities and alerting techniques

Visualization and Reporting

Dashboards & Reporting

Widget driven configurable dashboards, & reporting for security operations, executive, audit & compliance

Gurucul Miner

Gurucul Miner

Natural language contextual search for investigations

Risk Response Workflow

Risk-Response Workflow

Response workflows for automated risk remediation

Gurucul UEBA Use Cases

Insider Threat Detection and Deterrence

Insider Threat Detection and Deterrence

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. This enables you to easily detect and predict abnormal user behavior associated with potential sabotage, data theft or misuse.

Account Compromise, Hijacking and Sharing

Account Compromise, Hijacking and Sharing

Detect attacks using machine learning 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 of an account and corresponding transactions.

Privileged Access Abuse

Privileged Access Abuse

Monitor privileged accounts with contextual information around who accesses your IP and regulated data. Provide risk-based alerting of anomalous behavior and the ability to prevent and deter a threat before it occurs.

Data Exfiltration, DLP and IP Protection

Data Exfiltration, DLP and IP Protection

Identify data exfiltration and protect intellectual property by ingesting data sources such as DLP and data classification to uncover important data locations, access and application activity. Risk scoring DLP alerts is a primary benefit of UEBA machine learning because it significantly reduces alert fatigue and prioritizes ‘find-fix’ resources to prevent data breaches.

Trusted Host and Entity Compromise

Trusted Host and Entity Compromise

Detect advanced persistent threat (APT) attacks and attack vectors and predict data exfiltration by performing entity-centric anomaly detection. UEBA correlates a wide range of parameters associated with an entity, 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. This correlation facilitates detection of any anomalous activities or events.

SIEM and DLP Risk Intelligence

SIEM and DLP Risk Intelligence

Eliminate alert fatigue issue of SIEM and DLP security tools by aggregating the risk scores at the user and entity level, rather than generating a huge number of alerts at the transaction or event level. This use case employs bidirectional integration via APIs enabling SIEM and DLP data ingestion into UEBA as it provides risk scores back to these systems to allocate ‘find-fix’ resources.

Self Audit and ID Theft Detection

Self-Audit and ID Theft Detection

Deputize users into a collaborative relationship with security analysts to provide context and relevance not available to SOC teams. Employees, business partners and suppliers, agents in hub-spoke organizations, and in some cases, customers have one or more accounts with access entitlements to critical applications and data. A frequently issued self-audit report provides visibility for access, devices, locations and risk-scored anomalous behavior providing both detection and deterrence for end users.

Stateful Session Tracking

Stateful Session Tracking

Provide greater visibility into user activities across multiple resources or applications with stateful session tracking. UEBA builds and tracks the user session state, even when a user navigates across heterogeneous resources or applications using different accounts and devices at different times.  This enables the identification of valid IP switching due to transitions between wired and wireless networks, a workstation and a handheld/mobile device, or accessing enterprise resources from various onsite locations or remotely over VPN.

Anomalous Behavior and Watch Lists

Anomalous Behavior and Watch Lists

Address anomalous behavior with watch lists to quickly profile and maintain an eye on unknowns and apply escalating predictive risk scores. Watch lists come pre-defined within UEBA for common high-risk groups like new hires, departing users, terminated users, and high-risk users. These groups are easily accessed in dashboard drop-down menus to analyze risk scores, anomalies, accounts, access, activity and timelines.

Hybrid Infrastructure

Hybrid Infrastructure

Provide UEBA platforms for both on-premises and cloud-based applications. Remove the need for any big data transfers between environments. These transfers can be expensive and impede the use of important data for advanced security analytics. Our CASB API architecture enables direct data collection from cloud data sources while providing users a transparent access experience from any location. 

Gurucul UEBA Benefits

Results are the most important benefit. We are not threat hunters, we are threat finders. We’ve done all the hunting for you.

Empowered Security Capabilities and Quality

machine learning with big data for ueba security analytics

The mature capabilities of UEBA provide robust and optimal advanced security analytics. It applies across a range of on-premises and hybrid environments, scoring the gray areas of unknowns and minimizing false positives. The result is improving the focus of ‘find-fix’ resources and optimizing the time of security analysts, efficiency in the SOC, and making operations and people more productive.

Extended and Optimized, Discovery, Monitoring and Visibility

big data and account compromise

View the full context of a user’s access and activities, both legitimate and anomalous. Obtain a combined 360-degree view for identity, and risk-scored behavior anomalies driven by machine learning.

Improved Productivity and Cost Savings


Provide risk-ranked alerts with contextual visibility for the security team to shorten the prevention, detection, investigation, and remediation cycle of risks and threats. Deliver a manageable number of true positives that allows businesses to combat threats effectively. By having holistic visibility across all environments, users and devices, SOC teams’ efficiencies are maximized. In addition, as enterprises migrate to cloud applications, the ability to expand platforms without adoption of additional solutions helps to minimize costs.

Gurucul really stood out because the analytics engine was the most powerful. The machine learning algorithms are the strongest. We saw results very, very quickly. There’s an amazing value for this type of solution.

– William Scandrett, CISO, Allina Health


Read the blog Series ‘ABCs of UEBA’    

UEBA eBook (ABCs of UEBA)

User and Entity Behavior Analytics Use Cases

WP-UEBA Use Cases

Best Behaviour Analytics/Enterprise Threat Detection

SC Awards Europe 2020 - Best Behaviour Analytics/Enterprise Threat Detection

Using User & Entity Behavior Analytics to Stop Advanced Threats

Webinar-Using User & Entity Behavior Analytics to Stop Advanced Threats
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