Threat Analytics Platform (TAP)

Threat Analytics Platform (TAP)

• Millions of alerts are generated by best-of-breed technologies in organizations today.
• Writing rules and policies to find actionable events addresses only part of the problem.
• You can only write rules to look for issues you know, but what about the unknown?
• Monitoring insiders with advanced machine learning delivers holistic anomalous behavior alerts.
• Achieve comprehensive c security.

With perimeter disappearing and identity emerging as a threat plane, traditional security strategy can no longer cope in this new reality. In addition, there is too much data for humans to manage. A force multiplier is required. Machine learning models can surpass human capability for large volumes and variety of data to find high-order interactions and patterns in data for complex problems such as insider threats, compromised accounts and fraudulent activity. The advanced data science of user and entity behavior analytics, along with identity analytics, delivers the force multiplier needed for IT security teams.

Why Choose Gurucul Threat Analytics?

Protects intellectual property, prevents data exfiltration and predicts, detects and deters insider threats and cyber fraud.

Prevents ID theft through risk-scored event timelines and end user self-audits.

Improves data loss prevention (DLP) intelligence with risk-scored alerts based on behavior analytics.

Detects high privileged account abuse, account hijacking and anomalous activity.

Enhances security information and event management (SIEM) and security analytics intelligence.

Optimizes security resources and time with self-learning and self-training machine learning algorithms.

Gurucul Threat Analytics (TAP)

Gurucul’s Threat Analytics is built on our core architecture PIBAE (Predictive Identity-Based Behavior Anomaly Engine), which offers a broad array of user and entity behavior analytics (UEBA) features, driven by mature machine learning, drawing rich critical context from big data.

Designed to Identify Behavior Anomalies

Designed to Identify Behavior Anomalies

Self-training algorithms are tailored to identify learned anomalous behaviors immediately upon deploying the technology

Detailed Insight into all access and activity Anomalous Behaviors

Detailed Insight into all Anomalous Behaviors – Endpoints, Applications, Devices, and Users
Machine learning algorithms are executed on 250+ attributes to build different anomalous behavior profiles across the entities.


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 outsider.

Advanced Visualization & Workflow Centric UI

Advanced Visualization & Workflow Centric UI
Observe and analyze the threats for faster incident response and customize the views based on your operational needs.

What makes Gurucul Threat Analytics more effective?

Gurucul Threat Analytics’ core architecture is built on PIBAE™ (Predictive Identity-based Behavior Anomaly Engine)

Largest library of machine learning algorithms

Most granular and self-tuning risk modeling capabilities

Flexible metadata framework

Signature-less technology

Fuzzy logic-based link analysis

Built to scale using big data foundation


Gurucul Threat Analytics Successes


Most advanced and proven UEBA solution is here

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