Threat Analytics Platform Datasheet

Threat Analytics Platform

Intelligent User Behavior Analytics to deter, detect, and block insider threats, compromised accounts, and fraud


Millions of alerts are generated by best-of-breed technologies deployed within your organization and writing correlation rules and policies to find actionable events can only address one part of the problem statement – you can write rules to look for issues you know but what about the unknown?


Gurucul’s Threat Analytics Platform is built upon our core architecture PIBAE (Predictive Identity Based Behavior Anomaly Engine) to identify anomalous behaviors of malicious insiders and comprised accounts by leveraging contextual identity based behavior analytics, machine learning, and peer group modeling. Our big data enabled solution provides ONE STOP approach to protecting an organization’s intellectual property, sensitive information, and deterring an attacker from an under-the-radar cyber campaign or sophisticated insider activity like IP Theft, Brand Damage, and Fraud.


  • Predict, detect, and deter Insider Threats & Fraud
  • Proactive and actionable alerting on anomalous behaviors
  • Rapid and Enhanced ROI from Defense-In-Depth Solutions (e.g. SIEM, DLP)
  • Reduce the investigation time by more than 83% using contextual identity and visual investigation
  • Proactively find and stop data exfiltration
  • Soundproof cyber security infrastructure using Machine learning and intelligence driven big data security analytics

Gurucul’s Threat Analytics Platform is built upon our core architecture PIBAE to identify anomalous behaviors of malicious insiders


Powered by Predictive Identity Based Behavior Anomaly Engine that provides:

  • Library of Machine Learning Algorithms
  • Flexible Meta Data Framework
  • Fuzzy Logic Based Identity Correlation
  • Most Granular & Self Tuning Risk Modeling Capabilities
  • Signature-Less Technology
  • Built for Scale Using Big Data Foundation

Purpose Built to Identify Day Zero Anomalies

Self-training algorithms are tailored to identify learned anomalous behaviors immediately upon deploying the technology. (Insider Threat Count Screen)

Detailed Insight into All Anomalous Behaviors – Endpoints, Applications, Devices, and Users

Machine learning algorithms are executed on 254 attributes to build different anomalous behavior profiles across the entities. (Resource Risk Score Screen)

Context Aware Visibility of An Attack Lifecycle

Out of the box timeline view to highlight the anatomy of an advanced attack whether it be an insider or external. (Timeline View) Advanced Visualization & Workflow Centric UI Visually see and analyze the threat for faster incident response and customize the views based on your operational needs. (Custom Visualization Screen)

Situational Awareness with 3rd Party Intelligence Feed and Threat Sharing

Gain additional context by integrating 3rd party feeds and share industry specific threat scenarios. (Share with Healthcare, Finance)

About Gurucul

Gurucul is dedicated to transforming the cyber security landscape using machine learning, intelligence-driven, big data security analytics. Using identity as a threat surface, Gurucul provides Actionable Risk Intelligence™ to protect against targeted attacks and under-the-radar cyber campaigns. Gurucul is able to proactively detect, prevent and deter advanced insider threats, fraud, and external threats to system accounts and devices using sophisticated self-learning, advanced behavior and anomaly detection algorithms.

Gurucul is backed by a strong advisory board comprising of fortune 500 CISOs, world renowned experts in government intelligence and cyber security. The company was founded by seasoned entrepreneurs with a proven track record of introducing industry changing enterprise security solutions. Their mission is to deliver rapid results to any organization that desires to protect its intellectual property, regulated information, and brand reputation.

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