Varonis Atlas: AI Data Security Platform

Organizations are rapidly integrating AI agents, copilots, and large language models into their core workflows. These systems operate at machine speed, reading and writing sensitive data, which fundamentally shifts the security paradigm. The challenge is that many companies lack visibility into which AI systems are deployed, what data they can access, and whether their use complies with new regulations. Varonis Atlas directly addresses this critical gap as a comprehensive AI Security Platform. It provides the essential visibility and control needed to safely adopt AI at scale, offering the fastest path to secure and trustworthy implementation.

The platform is unique in its ability to manage the complete AI security lifecycle within a single solution. It connects to any AI system an organization uses, from hosted platforms and custom LLMs to agentic frameworks and embedded chatbots. Crucially, Atlas is built on the Varonis Data Security Platform, integrating deep data context that standalone tools cannot provide. This connection is vital because AI security must be rooted in data security. As Yaki Faitelson, CEO and Co-founder of Varonis, states, “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.”

Industry analysis underscores the urgency. Research indicates over half of organizations are already deploying or planning to deploy AI agents, with predictions that AI security platforms will be essential for securing development in nearly a third of enterprises. The risks are significant: autonomous agents can continuously modify data, often with overly broad and poorly understood access. A single misconfiguration can lead to a major data breach or substantial compliance fines.

Varonis Atlas secures everything an organization builds and runs with AI through several integrated capabilities. It begins with continuous AI Inventory and Shadow AI discovery, scanning cloud accounts and code repositories to build a living inventory of all AI assets, including unsanctioned shadow AI. This goes beyond surface-level discovery to catalog agents, models, tools, and supporting infrastructure.

The platform’s AI Security Posture Management (AI-SPM) continuously assesses systems for vulnerabilities, misconfigurations, and sensitive data exposure across the entire AI stack. Findings are enriched with data sensitivity context, exposing real business risk rather than just technical flaws. To proactively uncover vulnerabilities, Atlas performs AI Pen Testing by executing adversarial prompts and dynamic attacks against live LLM endpoints, simulating real-world threats like prompt injection and jailbreaks.

For runtime protection, AI Runtime Guardrails are enforced through an AI Gateway that inspects prompts, responses, and agent actions in real time. This prevents sensitive data leakage and blocks noncompliant behavior without altering the underlying application. To meet regulatory demands, AI Compliance and Governance features map systems to frameworks like the EU AI Act, generating audit-ready reports based on live system evidence rather than static questionnaires.

Recognizing that risk extends beyond internal systems, Atlas includes AI Third-Party Risk Management (AI TPRM). It continuously assesses external AI vendors by combining AI Bills of Materials with questionnaire data, providing ongoing visibility into supply chain risk. For operational oversight, AI Activity Monitoring delivers end-to-end visibility into production AI behavior, capturing full execution context within the customer’s environment for auditability.

Finally, AI Detection & Response (AIDR) identifies malicious or noncompliant behavior in real time. It uses AI-native threat detection to understand specific attack techniques, generating actionable alerts and blocking activity when necessary. These detections are unified with data security context, allowing teams to prioritize incidents based on actual business impact.

AI security cannot be effective as a collection of point solutions. It requires a unified approach that intrinsically understands both how AI behaves and the data it can access. Ron Bennatan, VP of AI and Data Security Strategy at Varonis, explains the advantage: “Most AI security tools are fragmented and data-blind. They can inventory your AI systems or monitor prompts, but they can’t see what sensitive data AI is accessing or control what it does with that data. That’s the real risk, and is exactly what Atlas and the Varonis Data Security Platform solve together.”