Identity is a critical component of a Zero Trust approach to network security. Finely tuned models avoid false positives, and a unique feedback loop ensures fast and accurate threat prevention as attacks happen – all without sacrificing performance. Our inline deep learning system analyzes live traffic, detecting and preventing today’s most sophisticated attacks, including portable executables, phishing, malicious JavaScript and fileless attacks. With inline prevention, the PA-Series automatically prevents initial infections from never-before-seen threats without requiring cloud-based or offline analysis for the majority of malware variant threats, reducing the time between visibility and prevention to near zero. Our ML-Powered NGFWs use embedded ML algorithms to enable line-speed classification, inspecting files at download and blocking malicious files before they can cause harm. Plus, using solutions that pull files offline for inspection creates bottlenecks, hinders productivity and can’t scale.

This leaves security professionals struggling to keep up since manually adding signatures cannot be done fast enough to prevent attacks in real time.

Attackers frequently bypass traditional signature-based security, modifying existing threats that then show up as unknown signatures.

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