Predictive Security Analytics to Detect Unkown Threats and Reduce Access Risks

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Gurucul Risk Analytics (GRA) addresses a major cause of modern threats: the compromise or misuse of identities. Users have multiple accounts and entitlements, often in excess, providing an opportunistic environment for cyber attacks and insider threats. While a CIO’s goal is widespread data access and enablement, CISOs struggle with declarative defenses and controls. The outcome is data breaches and escalating costs as preventive defenses decline in effectiveness. The rapidly growing volume of security data needs data science.

GRA is a behavior based security analytics and intelligence platform on open choice of big data. Gurucul Data Mine is an open source big data backend used to correlate, link and store data from applications, platforms, netflow, threat intelligence, and other security solutions. GRA uses this contextual information for machine learning, behavior analytics and deep learning to provide real-time risk prioritized alerts for incident analysis and automated responses for enterprise and cloud.

Why Choose Gurucul Risk Analytics?

Model good behavior to expose unknown bad behavior through peer groups, clustering and outliers.

Analyze access and its abuse with identity-centric behavior analytics from big data.

Detect insider threats, account hijacking and abuse, plus data exfiltration.

Reduce and manage the account surface area with risk-based access controls.

Provide behavior analytics for on-premises and cloud application hybrid deployments.

Leverage predictive security analytics to risk-score incidents and drive ‘find-fix’ focus.

Gurucul Risk Analytics Components

Gurucul Risk Analytics has four components to address user, entity, identity and fraud use, uniquely combining data science and machine learning models to deliver actionable behavior based security analytics and intelligence.

Provides behavior-based predictive risk scoring

• Risk-scored timeline to predict, detect and deter insider and advanced threats
• Identity-based threat plane behavior analysis for account hijacking and abuse
• Proactive and actionable alerting for anomalous behavior and risk scores
• High privileged access anomaly detection for misuse, sharing, or takeover
• Customizable dashboards, configurable policies and risk model optimization
• Work-centric UI with case management, or input to third-party solutions
• Self-audit portal deputizes users for risk awareness to detect identity theft

What makes Gurucul Risk Analytics more effective?

GRA’s core architecture is built on PIBAE™ (Predictive Identity-based Behavior Anomaly Engine)

Industry’s largest library of machine learning algorithms

Awareness of time-based norms such as accepted workflows and operational changes

Built for scale with open choice big data

Peerless granular and self-tuning risk modeling capabilities

Out-of-the-box algorithms learn anomalous behaviors immediately upon deployment

Dynamic peer groups improve clustering and outlier machine learning accuracy



Create custom machine learning models without coding and needing only a minimal knowledge of data science. Gurucul STUDIOTM provides a step-by-step graphical interface to select attributes, train models, create baselines, set prediction thresholds and define feedback loops. As part of Gurucul Risk Analytics (GRA), STUDIO supports an open choice for big data and a flex data connector to ingest any on-premises or cloud data source for desired attributes. Step outside the black box and create custom models for your own predictive security analytics needs

Read the Datasheet

“Gurucul Risk Analytics reduced the number of accounts and entitlements by 83%, plus defined intelligent roles and provided dynamic access provisioning, using a behavior-based risk context.”

– AVP Cyber Security, IT Services Company

Gurucul Risk Analytics

SC Mag Award US
SC Mag Award Europe

Winner of the Best Behavior Analytics / Enterprise Threat Detection Trust Award is here.

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