Zscaler announced the acquisition of Israeli startup Avalor to enhance its ability to provide artificial intelligence (AI)-driven security analysis and decision-making. The deal was reportedly valued at $350 million.

Founded in 2022, Avalor has raised $30 million in two funding rounds. It emerged from stealth in April last year and raised a $25 million Series A funding round led by TCV and Salesforce Ventures. The acquisition closed on March 13, with the majority of the purchase price paid in cash and a portion in equity subject to vesting conditions, Zscaler said.

Avalor offers its Data Fabric for Security to ingest, normalize and unify data across enterprise security and business systems to deliver actionable insights, analytics and operational efficiencies.

This integration with Zscaler’s Zero Trust Exchange cloud security platform is designed to help customers proactively identify and predict critical vulnerabilities and improve operational efficiencies by leveraging more than 400 billion daily transactions processed by Zscaler with Avalaor’s 150 pre-built integrations.

“AI is only as good as the underlying data, and many solutions lack the additional context and knowledge from data sources across the enterprise to truly leverage security-specific AI models,” Jay Chaudhry, CEO, chairman, and founder at Zscaler, said in a statement. “Zscaler operates the world’s largest security cloud with the most relevant data to train security-specific large language models (LLMs) and with the Avalor acquisition, we can more effectively identify vulnerabilities, while predicting and preventing breaches.”

The security vendor aims to enhance and fully automate AI-driven analytics and decision-making in real time without the complexity of data aggregation and collection.

“We have long understood that being able to make sense of all the disparate security data sources in an organization is essential to understanding and improving risk posture, that’s why we delivered the industry's first Data Fabric for Security to provide that aggregated platform,” said Avalor CEO and co-founder Raanan Raz.

“The first application running on our Data Fabric is our Unified Vulnerability Management (UVM) module,” Raz added. “By combining the Zscaler proprietary data sets with the 150 third-party sources Avalor supports, we will be in a prime position to enhance our UVM capabilities and create new applications with additional cyber protection insights.”

Zscaler doubles down on AI-enabled security

Zscaler’s threat research and incident response team has been harnessing the power of AI and LLMs to predict breach paths by performing impact analysis while recommending policies to prevent future attacks. The vendor plans to productize this prediction capability, which enables recommend preventive measures and potential policy changes.

The vendor collects more than 300 billion records and transactions per day from multiple sources, including network endpoints, partner feeds and threat signals, which can be fed into its proprietary AI engines and LLMs to continuously learn from changing cloud-based policies and logs, allowing Zscaler to understand and predict how and when malware will progress, Zscaler product manager Sanjay Kalra told SDxCentral in an earlier interview.

Zscaler also introduced Zscaler Navigator at last year’s Zenith Live event. It is a generative AI-powered interface that enables customers to interact with Zscaler products and access relevant documentation details using natural language and multimodel data loss protection (DLP) that protect customers’ data from leakage across various media formats beyond text and images, such as video and audio formats.

In 2022, the vendor announced AI and machine learning capabilities for its zero-trust security platform to make it easier for organizations to implement security services edge (SSE) and adopt zero-trust architecture while maintaining user experience. The updates include AI-powered phishing prevention, AI-powered segmentation, autonomous risk-based policy engine, and AI-powered root cause analysis.