Zscaler to enhance AI into its security tools

The San Jose, California-based cloud security OEM, Zscaler has acquired Tel Aviv-based Avalor, the developers of a patented Data Fabric for Security. Avalor will help Zscaler strengthen its data quality and model efficiency to outpace AI weaponization by threat actors.

This acquisition marks another major leap forward in AI to increase the velocity of new innovations that will disrupt legacy approaches in the cybersecurity industry.

Avalor’s technology - which allows for real-time aggregation and contextualization of data from multiple sources - will advance the precision, speed and effectiveness of the AI capabilities in the Zscaler Zero Trust Exchange.

With the addition of Avalor’s technology, our cloud security platform will be able to systematically ingest, harmonize, and action high-value data from 400 billion daily cloud transactions, including security findings and business context from more than 150 third-party sources. Says, Zscaler founder and CEO Jay Chaudhry.

The combination of Zscaler and Avalor will use advanced AI analytics to simplify security operations and provide comprehensive risk profiles, according to Chief Technology Officer Syam Nair.

This will provide our customers with a powerful AI advantage that delivers the insights and intelligence to stay ahead of emerging, large-scale cyber attacks. In the world of AI, data is gold - and the ability to harness the power of AI to curate and correlate that data at massive scale is crucial to unlocking its full potential.

In addition to generating insights and findings that help organizations proactively identify critical vulnerabilities, AI-powered solutions can provide a holistic view of their overall cyber risk posture, serving as the foundation for future advancements.

With 16 years of proven leadership operating the world’s largest security cloud, Zscaler’s massive data set combined with Avalor’s pioneering technology gives us the ability to deliver the most powerful, AI-driven cybersecurity solutions at scale.