Automating Incident Response with Machine Learning

Presented by

Mike Parkin, Director of Technical Marketing, Gurucul

About this talk

Incident Response is a key responsibility of any SecOps team whether they are sited locally, operating as a distributed group, or a function provided by an MSSP. With the sheer number of incidents they can face, it can be difficult for the team to stay ahead of the game. Fortunately, automation, based on AI-driven security analytics, can lighten the load and make the team more efficient, more effective, and better able to handle their workload. By applying artificial intelligence, the system can adapt and react to new threats even as they're developing. But beyond that, Machine Learning lets the system evolve over time, adjusting itself to the operational environment to optimize performance and efficacy. Join us as we explain how Gurucul's Unified Risk and Security Analytics platform uses machine learning and artificial intelligence to deliver advanced automated incident response.

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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.