GoKawiilVaronis is proud to announce the general availability of Varonis Atlas, an end-to-end AI Security Platform that helps organizations see and control AI across the enterprise.
Atlas is the only platform that covers the entire AI security lifecycle — from discovery and posture management to runtime protection and compliance — in a single solution. It connects to any AI system organizations build or run: hosted AI platforms, custom LLMs, agentic frameworks, chatbots, and embedded AI. And because Atlas is built on the Varonis Data Security Platform, it brings data context that no standalone AI security tool can match.
“AI completely disrupts the enterprise security model. Instead of humans clicking through UIs, agents are accessing data directly — and this places data and AI security front and center,” said Yaki Faitelson, CEO and Co-founder of Varonis. “If you can’t see what AI systems you have and what sensitive data they can reach, you can’t safely use AI at scale. Varonis Atlas gives organizations the fastest path to safe and trustworthy AI.”
Your fastest path to safe and trustworthy AI
AI agents, copilots, and LLMs are now embedded in enterprise workflows. They read, write, and act on data at machine speed. However, most organizations don’t know which AI systems they have, what those systems can access, or whether they’re compliant with emerging regulations.
Gartner® recently wrote a report, the Future of AI Security is in Securing Agent Actions, Not Prompts, and in their analysis, researchers discovered that over 50% of organizations have already begun deploying or plan to deploy AI agents. Organizations are also building with AI.
The report predicts AI security platforms will be used in 30% of organizations to secure agent development within AI-native software engineering, as the growing majority of enterprise software relies on agentic coding tools.
As enterprises deploy more autonomous and agentic AI systems, risk escalates:
Agents read, write, create, and modify data continuously and at machine speed
Data access is often too broad and poorly understood
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