MIND Launches Data-Centric DLP Platform for Agentic AI Security

New updates have been reported about MIND.

MIND has introduced a new product, DLP for Agentic AI, positioning the company squarely at the intersection of data security and rapidly emerging autonomous AI use cases. The platform is designed to let enterprises adopt agentic AI at scale while maintaining strict control over how sensitive data is discovered, accessed, and used across SaaS applications, endpoints, homegrown systems, and third-party tools. Instead of focusing on models or outputs, MIND’s approach centers on data as the core asset, providing autonomous discovery, classification, and protection of sensitive information before AI agents can interact with it. CEO and Co-Founder Eran Barak framed the launch as essential to realizing AI’s promised business outcomes, arguing that innovation and productivity gains depend on data being properly protected, governed, and understood.

The new DLP for Agentic AI capability gives security and risk leaders enterprise-wide visibility into which AI agents are active, what data they are accessing, and where behavior may be risky, with real-time monitoring, alerting, and automated remediation. This is aimed at closing emerging gaps created by autonomous AI workflows, which traditional, human-centric DLP tools were not built to handle. Early enterprise users are applying MIND’s platform to enable generative and agentic AI while preserving compliance and minimizing data leakage risk, indicating near-term commercial relevance as AI investments accelerate. By focusing on context-aware automation and controls that do not unduly slow productivity, MIND is positioning itself as a foundational security layer for AI-driven operations, with potential to drive increased platform adoption, deeper enterprise penetration, and strategic differentiation in the data security market as agentic AI moves from experimentation to production at scale.