Live Demo: Predictive Capabilities of Fidelis Elevate XDR

Using ML/AI and Metadata to Detect Anomalous Activity and Produce Actionable Alerts for Known/Unknown Threats Collecting hundreds of log types and analyzing them has shown to be an ineffective approach to threat detection, even when applying behavior analysis and machine learning. The converse entails capturing the raw data and facing delays to decode and reassemble, plus high storage expenses. Logs lack content and context, and the raw data itself is too slow to analyze and expensive if you require a time span of months or a year. And while most security tools focus on specific detection techniques, Fidelis leverages more than 20 different detection methods… including endpoint and asset terrain, deep session and deep packet inspection, sandboxing, malware detection, metadata analytics, threat intelligence and more… making it that much harder for attackers to evade. Join Martha Goodwin, Security Engineer for a demo of Fidelis Network and find out how to: *Use ML and Anomaly Detection *Use Metadata Analysis to search for Known/Unknowns *Create Analytical rules to predict possible incidents