Gurucul Cloud Analytics

As enterprises rapidly adopt cloud technologies, they face the same security challenges with cloud applications they did with in-house applications and systems. In many cases more. It is increasingly difficult to control user access and activity, as well as data movement and maintenance for these systems. While some operational and infrastructure issues are less relevant in the cloud, concerns relating to data breaches, data loss, account hijacking, malicious insiders, and shared technology are even more prevalent, representing constantly growing risks.

Why Choose Gurucul Cloud Analytics?

Provides full insight into cloud applications with contextual views of an identity, its access and associated activity.

Highlights behavioral anomalies, to identify insider threats, compromised accounts, compliance violations, data leakage.

Provides an organization with continuous insight into its cloud infrastructure.

Facilitates security incident investigation and forensics.

Employs big data machine learning, contextual access, and user behavior modeling for state-of-the art intelligent security analytics.

Reduces the attack surface for accounts, unnecessary access rights and privileges, and identifies, predicts and prevents breaches.

Gurucul’s Cloud Analytics Platform (CAP) – Features

The Gurucul Cloud Analytics Platform is an API-based CASB (Cloud Access Security Broker) built on our core architecture PIBAE (Predictive Identity-Based Behavior Anomaly Engine), which offers a rich array of predictive security analytic features.


360° View of Identity, Access, and Activity
Correlate data across multiple cloud applications to create contextual identity – who is the user, what access they have, and what is their activity

Detailed Insight into all access and activity Anomalous Behaviors

Detailed Insight into all Anomalous Behaviors – Access and Activity
Machine learning algorithms are executed on access and usage attributes to build cloud-centric anomalous behavior profiles across the enterprise.

Designed to Identify Behavior Anomalies

Designed to Identify Behavior Anomalies
Self-training algorithms are tailored to identify learned anomalous behaviors immediately upon technology deployment.


Context-Aware Visibility of an Attack Lifecycle
Out-of-the-box timeline view to highlights the anatomy of an advanced attack whether, it be an insider or outsider.

What makes Gurucul Cloud Analytics more effective?

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

Industry’s largest library of machine learning algorithms

Peerless 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 Cloud Analytics Successes


The one-stop analytics solution across all cloud platforms is here.

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