Enterprises face a clear mandate: integrate AI or fall behind. Yet as organizations deploy autonomous agents into production systems, they’re encountering a fundamental security challenge. AI agents don’t just analyze data—they act on it at machine speed, and most security infrastructures weren’t built for this reality.

Varonis’s acquisition of AllTrue.ai in February 2026 represents a significant shift in how enterprises approach AI security. CEO Yaki Faitelson now positions the company to secure the entire AI lifecycle, forcing the industry to address a critical question: how do we govern systems that operate autonomously?

Machine Speed Eliminates the Human Buffer

Enterprise risk has fundamentally changed. Organizations have moved from AI models that analyze information to autonomous agents that read, write, modify, and execute actions without human oversight. Traditional security tools struggle with this shift because they’re designed for human-scale interactions.

When humans interact with data, physical and temporal constraints create natural intervention points. AI agents operate without these limits. They execute actions in milliseconds across vast datasets, often with broad permissions but minimal oversight. Traditional identity and access management systems lack the capabilities to map and govern non-human identities at this scale. Organizations need to distinguish between legitimate automated processes and potentially rogue agents—a capability most current security frameworks don’t provide.

Data Layer Security Matters More Than Model Security

Many security teams focus on the AI front end—prompts, interfaces, and model behavior. This approach misses the core issue. AI agents derive both their value and their risk profile from the enterprise data they access.

Ron Bennatan, CEO and co-founder of AllTrue.ai, frames it directly: “Most AI security efforts focus on models and prompts. But the real value, and risk, of AI is related to the enterprise data AI can access. Varonis pioneered the data-centric security approach that must be the foundation of AI security.”

Model sophistication becomes irrelevant when agents have excessive privileges. Organizations must root security in the data itself, ensuring autonomous systems operate under least-privilege principles regardless of their technical capabilities.

Shadow AI Demands Visibility and Governance

Organizations currently face a surge in ungoverned AI implementations—chatbots, custom models, and AI-powered tools deployed across departments without formal oversight. The Varonis-AllTrue.ai combination addresses this through AI Security Posture Management (AI-SPM), AI Security Testing, and AI Detection and Response (AIDR).

Without visibility into these systems, organizations remain vulnerable to attack vectors that bypass conventional defenses. Varonis Threat Labs recently discovered “Reprompt,” an attack that bypasses Microsoft Copilot’s safety controls to exfiltrate secrets undetected. This example highlights the new threat landscape:

• Prompt Injection: Attackers manipulate inputs to override model instructions.
• Jailbreaks: Circumventing safety filters to force non-compliant behavior.
• Misconfigurations: Improper permissions allowing unauthorized data modification.
• Data Leakage: Unintentional exposure of proprietary information through AI interactions.

Real-Time Protection Replaces Post-Incident Analysis

Organizations need more than observability—seeing what went wrong after the fact no longer suffices. AI Trust, Risk, and Security Management (AI-TRiSM) prioritizes runtime protection, intercepting unsafe actions before execution.

A tool-agnostic AI gateway provides the foundation for this approach. It filters or blocks risky actions in real-time, regardless of whether organizations use Microsoft Copilot, ChatGPT Enterprise, custom models, or internal tools. This unified protection layer ensures autonomous agents remain bounded by corporate policy and regulatory requirements as they execute.

What This Means for Enterprise AI

The Varonis-AllTrue.ai integration offers a framework for securing autonomous AI systems. By combining data and identity expertise with real-time enforcement capabilities, the platform addresses the gap between AI deployment velocity and security maturity.

Organizations deploying AI agents into business-critical processes must answer a practical question: do your current security controls provide adequate governance for systems that operate autonomously at machine speed? For most enterprises, the honest answer reveals significant work ahead.