Overview

Gurucul Risk Analytics (GRA) goes beyond rules, signatures and patterns with machine learning models based on big data from on-premises and cloud learning to detect abnormal user and entity behavior analytics (UEBA). GRA also includes the ability to reduce the surface area of access with identity analytics (IdA) with a risk-based approach for certifications, access requests and approvals, plus removing excess access, access outliers and cleaning-up orphan and dormant accounts. This holistic approach of UEBA and IdA together focuses on the compromise and misuse of identity, the root of modern threats.

GRA is built upon Hadoop and enables an open choice of big data to store data for value while leveraging 189 ready to use machine learning models for on-premises, cloud or hybrid environments.Over 30 data connectors speed ingestion of popular data sources, plus a flex connector enables any data source to ingest into GRA, no waiting on roadmaps or professional services. GRA focuses on 33 primary use cases for threats, access and cloud. More advanced customers can customize risk weightings plus develop their own machine learning models without coding within GRA.

For benefits, customers of GRA have seen the following results:

A financial firm reduced accounts and entitlements by 83% leveraging identity analytics (IdA). Adopting a risk-based approach the customer migrated to risk-based access certifications for requests and approvals and also deployed intelligent roles provided by GRA.

An insurance firm deployed Self Audits from GRA to over 60,000 end users to raise security awareness, provide deterrence and collaborate with users to detect identity theft and abuse. One privileged user detected an access anomaly when out of work on a weekday, the investigation later surfaced the account had been compromised for 3.5 years.