• 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
Self-training algorithms are tailored to identify learned anomalous behaviors immediately upon deploying the technology
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
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)
Gurucul Threat Analytics Successes
Gurucul’s unique Self-Audit™ feature, which deputizes users, has uncovered data exfiltration at a number of organizations.
UEBA has discovered high privileged access anomaly detection for misuse, sharing, or takeover with a growing number of customers.
Gurucul’s identity-based threat plane behavior analysis detects account hijacking and abuse.
More and more customers rely on the TAP risk-scored timeline to predict, detect and deter insider and advanced threats.
Customers claim Gurucul’s customizable dashboards, configurable policies and risk model optimization position their SOCs in the next generation of security analytics.