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Gartner identified Gurucul as a vendor that supports multiple use cases profiled in this research.
Gurucul has pioneered an entirely new security category that uses analytics and machine learning techniques to monitor user and device behaviors and detect threats that otherwise appear to be ‘normal’ activity to other protection technologies.
Using big data enabled machine learning models, Gurucul scours identity, accounts, access and activity to discover and risk score privileged access down to the entitlement level across on-premises, cloud and hybrid environments.
This latest addition to the company’s machine learning model library provides 360-degree visibility and risk-scoring of identities, accounts, access and activity in today’s borderless architectures.
Since that time, the Intel Security Innovation Alliance has executed on that strategy, doubling the number of partner companies that have existing, or are developing, technology integrations with McAfee Data Exchange Layer (DXL).
Machine learning models go beyond rules, patterns and signatures to identify access risks and unknown threats by detecting when users deviate from their and their peers normal base lines of activity.
Using identity data from Okta, Gurucul’s behavior-based machine learning models can identify and score the severity of access risks.
CAP is part of Gurucul Risk Analytics (GRA) that includes Threat and Access Analytics Platforms for complete visibility of cloud and on-premises hybrid environments.
Gurucul combines IAI to reduce excess access rights and outliers that open the door to phishing and account compromise attacks,