Gurucul Extends Behavior Based Security Analytics to Entire IT Stack Enabling Real-Time Automation of AI/ML Driven Security Controls
Summary
The new version of GRA also provides a new streamlined user experience that includes an open and flexible framework for personalizing widget-driven dashboards with a wide range of visualizations and canvas-based components to view, modify or build new behavior and threat models using Gurucul Studio™. Gurucul offers a real-time behavior analytics platform that uses open choice, “no cost” Big Data to collect high-frequency events / transactions and contextual metadata from the entire IT stack and run machine learning models that detect and risk-score suspicious activity. “For effective risk mitigation, a security analytics platform must be able to span the entire IT footprint of an organization and provide an open framework to create user defined entities, modify existing machine learning models and trigger risk-response actions in real-time,” said Nilesh Dherange, CTO of Gurucul. To detect advanced threats from external attackers and malicious insiders such as fraud, data exfiltration, and account compromise, Gurucul now has more than 1000 pre-packaged machine learning models. These include unsupervised, supervised and deep learning algorithms, as well as versions that are pre-tuned to predict and detect specific types of threats and for industry use cases such as finance, healthcare and retail.
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