Architectures like next-generation firewalls (NGFWs) that allow pass-through connections can’t quarantine content for ML analysis, which means malicious bytes will pass until a verdict is determined.
Zscaler Machine Learning quickly identifies threat patterns across volumes of data in order to block advanced threats without signatures or human interaction. By leveraging the size and scope of the Zscaler cloud, users and data can be protected in real time from emerging threats, such as polymorphic malware, spear phishing, and suspicious websites.
Zscaler Machine Learning closes security gaps by classifying new and unknown websites as they are being accessed, which improves policy enforcement and user experience.
Traditional NGFW pass-through architectures have limited resources to hold and analyze all packets in real time. Because Zscaler leverages cloud scale for inline inspection, every packet is captured and analyzed for malicious content in real time before endpoint delivery.
Many targeted and purpose-built phishing websites cannot be detected by signatures or unsophisticated ML. Zscaler’s inline machine learning models can detect these dangerous and unknown targeted phishing pages before they appear in the end user’s browser.
Traditional solutions have to crawl new and unclassified websites to properly classify them, which is inefficient and leaves users exposed to suspicious content. With Zscaler Machine Learning, web content is immediately classified upon user access, which enables improved enforcement of policies that block unclassified web pages.