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Gurucul Selected by United States Federal Government for Predictive Security Analytics Utilizing User Behavior and Identity AnalyticsGurucul, a leader in Predictive Security Analytics and Intelligence for on-premises and the cloud, today announced that one of the largest Federal Government Civilian agencies has selected the Gurucul Risk Analytics (GRA) platform to detect and prevent threats that otherwise appear as "normal" behavior to traditional security tools. The deployment, which will protect upwards of 100,000 users, is the largest implementation of user and entity behavior analytics (UEBA) by the Federal Government to date. "Security sensitive organizations like the US federal government recognize that analytics and machine learning can detect threats that traditional approaches cannot," said Saryu Nayyar, CEO of Gurucul. "Being chosen for the largest implementation of user and entity behavior analytics by a federal agency in history demonstrates that the Gurucul platform delivers capabilities as promised and possesses integration capabilities required for extracting security intelligence from big data." Gurucul was selected for its ability to support an unlimited number of users and entities; apply native machine learning capabilities for automated behavior baselining, anomay detection, and model development; and generate a dynamic risk score for each user/entity based on the analysis of simultaneously ingested data from multiple sources including Windows, Linux, and UNIX operating systems, and network devices such as Cisco, Brocade (News - Alert) and Juniper. Gurucul is changing the way enterprises protect themselves against cyber fraud, insider threats, account compromise, data exfiltration and external attacks. The company's advanced security analytics technology uses machine learning, anomaly detection and predictive risk-scoring algorithms to identify, predict and prevent breaches in real-time. Gurucul Risk Analytics (GRA) ingests and analyzes huge volumes of data generated when users and devices access and interact with business applications, whether they are in the company's own data center or hosted in the cloud. The platform monitors user and entity behaviors using machine learning algorithms to detect threats that appear as "normal" activity to traditional security products, such as hackers using login credentials stolen from authorized users, as well as malicious insiders like employees and contractors.
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