Cleaning House: Getting Rid of Malicious Insiders

Presented by

Jane Grafton, VP Marketing, Gurucul

About this talk

Insider Threats are a common concern for a lot of organizations, and Gurucul's Risk Analytics platform has a range of features that are specifically designed to handle the insider threat use case. The assumed challenge can be dealing with malicious insiders before they become an active threat, doing damage to the organization, its reputation, or it's customers. Machine Learning based security analytics can identify these malicious insiders by their behaviors and highlight the risk before they cross the line from a potential issue to an active threat. In many cases, malicious actors display telltale behaviors well before they act which means it's possible to identify the risk early enough to prevent an unhappy employee, or deliberate threat actor, from doing damage to the organization. Join us to explore how Gurucul's Machine Learning risk analytics platform can help you identify and remove malicious insiders before they generate a newsworthy incident.

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Gurucul is transforming enterprise security with user behavior based machine learning and predictive analytics. Using identity to monitor for threats, Gurucul provides Actionable Risk Intelligence™ to protect against targeted and under-the-radar attacks. Gurucul is able to proactively detect, prevent, and deter advanced insider threats, fraud and external threats to system accounts and devices using self-learning, behavioral anomaly detection algorithms. Gurucul is backed by an advisory board comprised of Fortune 500 CISOs, and world renowned-experts in government intelligence and cyber security. The company was founded by seasoned entrepreneurs with a proven track record of introducing industry changing enterprise security solutions. Our mission is to help organizations protect their intellectual property, regulated information, and brand reputation from insider threats and sophisticated external intrusions.