Recognizes patterns and efficiently handles large structured datasets at scale to analyze and block malicious threats.
Automates feature learning and efficiently handles unstructured datasets at scale to identify abstract patterns and detect evolving threats.
Creates human-like content such as text and images, which our models are trained on, to identify AI-generated threats.
The only solution to block unknown C2 attacks and exploit attempts in real time using Advanced Threat Prevention's industry-first, purpose-built inline deep learning models.
Safeguard your network from known threats, such as exploits, malware, spyware, and command- and- control attacks, with market-leading, researcher-grade signatures that don’t compromise performance.
Advanced Threat Prevention blocks threats at both the network and application layers, including port scans, buffer overflows and remote code execution, with a low tolerance for false positives.
Protect against the most recent and relevant malware with payload signatures, not hash, to block known and future variants of malware, and receive the latest security updates from Advanced WildFire® in seconds.
Leverage User-ID™, App-ID™ and Device-ID™ technology on our ML-Powered NGFWs to add context to all traffic on all ports so you never lose sight of a threat, regardless of the techniques used.
Add to your threat coverage with flexible Snort and Suricata rule conversion for customized protections.
Advanced Threat Prevention protects your network by providing multiple layers of prevention during each phase of an attack while leveraging deep and machine learning models to block evasive and unknown C2 and stop zero-day exploit attempts inline.
Learn how Advanced Threat Prevention provides the real-time, inline protection you need to secure your organization from even the most advanced and evasive threats.
HIGHER THROUGHOUT
ADVANCED ML MODELS
EVASIONS BLOCKED
