Predictive Identity based Behavior Anomaly Engine (πβαɛ)
Predictive Analytics And Day Zero Anomaly Detection Framework
The threat landscape is changing and a signature-less analytics approach which adapts to todays and future advanced threat scenarios is becoming a new necessity. Gurucul has introduced a next generation analytical framework called Predictive Identity Based Behavior Anomaly Engine [πβαɛ]. πβαɛ uses identity as the core and overlays activity, alerts, intelligence, and access to provide customers with predictive security analytics and day zero anomalies. This first of it’s kind framework combines user behavior intelligence, big data analytics, and leverages identity as a threat surface to provide Actionable Risk Intelligence™. πβαɛ is powered by Gurucul’s patented machine learning algorithms that run against hundreds of attributes to determine a baseline behavior for an entity, compare it against dynamically created peer groups to detect anomalous patterns. These patterns are matched against internal risk modelling algorithms to assert a risk score for an entity. By leveraging this framework, Gurucul’s products & solutions provide organizations with a proactive approach to not only respond and detect cybercriminal activity and under-the-radar cyber campaigns but also deter threats from insiders stealing intellectual property, executing fraud, or performing targeted attacks.
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Risky Science of Risk Assessment
Data is the currency of the digital age. It is the foundation for analytics. The value of data lies in the context it provides and the timeliness of its content. Information decline is an important concern for data scientists in predictive security analytics.