Using Machine Learning to Hunt Security Threats

As the global cyber threat spectrum continues to expand exponentially, organisations are increasingly turning to AI/machine learning-based analytics to enhance threat detection and minimise the operational overheads associated with maintaining conventional static detection rules in large-scale SOC.

While the potential for AI/machine learning to improve Security Operations is vast, its implementation is often hindered by the complexity of cyber security big data and the multitude of attacker techniques.

In this webinar, you will learn how the Information Security Division at Saudi Aramco’s EXPEC Computer Center (ECC), one of the world’s largest integrated energy and chemical companies, leverages Splunk’s machine learning toolkit to identify and hunt security threats in real-time, whilst enhancing its security posture.

Webinar duration: 60 minutes

Join Saudi Aramco's Muath Saleh (Cyber Security Engineer, ECC-SOC) and Hafiz Farooq (Cyber Security Architect, ECC-SOC) who will explain:

• The mission and security operations model of the Saudi Aramco ECC SOC
• The processes and skills in place within the SOC
• Top Threat hunting practices for insider threats
• The top algorithms utilised best for Anomaly Detection

Can’t attend live? Register anyway and we’ll send you the recording after the webinar!