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New machine-learning algorithm protects data against insider threats

GRC Announcements | June 28, 2017

Imperva, a provider of cyber-security solutions that protect business-critical data and applications, recently launched a new algorithm to automatically place individuals and their cross-functional peers into “virtual” working groups based on interactions with enterprise files to identify unusual user access patterns. This new Dynamic Peer Group Analysis algorithm proves an intelligent approach to permissions management that helps protect data against insider threats.

The new machine learning algorithm in CounterBreach 2.0 automatically identifies ad-hoc and cross-functional working groups, assigning users into peer groups. CounterBreach then analyzes user behavior and flags risky file access from unrelated individuals—such as an engineering manager accessing a sensitive finance budget file or an engineering file not associated with his peer group, which he has rights to, but is not accessed by anyone in his virtual peer group. The result is a dynamic approach to file security that allows employees to freely access data, yet saves IT teams time and enhances the security of file data.

“Traditionally, permissions management is manual, time-consuming, and often inaccurate or outdated, creating a gap in which data contained in files can be lost, stolen, or misused by malicious, careless, or compromised users,” said Amichai Shulman, CTO and co-founder of Imperva. “Detection and containment of insider threats requires an understanding of both users and how they use enterprise data. CounterBreach 2.0 leverages machine learning for an intelligent approach to permissions management that reduces the risk of insider threats, safeguards data and improves the overall security posture of the organization.”