MIND Debuts Autonomous DLP to Strengthen Agentic AI Security
DLP at AI speed enables enterprises to safely rely on agentic AI
Enterprises are moving quickly to adopt agentic AI to drive real business outcomes, including faster decision-making, increased productivity and new operational efficiencies. But as AI systems become more autonomous, those outcomes depend on one critical factor: whether organizations can trust how their data is accessed, used and controlled.
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Today, MIND announced DLP for Agentic AI, a data-centric approach to AI security designed to help organizations safely achieve the business value of agentic AI by ensuring sensitive data and AI systems interact safely and responsibly.
Agentic AI can autonomously create, access, transform and share data across SaaS applications, local devices, homegrown systems and third-party tools. While this unlocks meaningful gains in speed and scale, it also introduces new risks. Without clear visibility and controls, data security gaps can undermine AI initiatives, slow adoption and put business outcomes at risk.
“Organizations aren’t adopting AI for experimentation alone. They’re adopting it to drive innovation and outcomes,” said Eran Barak, Co-Founder and CEO at MIND. “Those only materialize when data is protected, understood and governed. MIND’s DLP for Agentic AI gives security teams the controls they need to protect sensitive data used by AI agents.”
Data Security as the Foundation for AI Outcomes
As organizations evaluate how to secure agentic AI, new security categories are appearing. However, most of these emerging approaches fail to secure the critical foundation that Agentic AI relies on: the data itself.
MIND’s DLP for Agentic AI starts with the belief that business outcomes depend on whether AI systems have the right access to the right data at any point in time. Instead of securing models or reacting to outputs, MIND ensures sensitive data is understood, governed and protected before any AI agent can access or act on it.
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With this data-centric approach, organizations can:
- Identify which AI agents are active across the enterprise and on endpoints, including embedded SaaS capabilities, homegrown agents and third-party tools
- Detect risky data access by AI agents, monitor behavior in real time and autonomously alert and remediate issues as they emerge
- Apply the right controls so data and agentic AI interact safely, without slowing productivity or innovation
By putting data security and controls at the center of AI adoption, MIND helps organizations turn AI potential into measurable business results with the right guardrails.
Supporting Enterprise AI in Practice
Customers are already using MIND to support enterprise AI initiatives and the secure use of GenAI while maintaining strong data security.
“It’s crucial that we enable the business to benefit from the efficiencies that AI can offer without compromising the security of our data,” said Zac Fletcher, Assistant Vice President of IT Security at Service Corporation International. “The MIND platform gives me the visibility, capabilities and confidence to do this.”
Built for an Agentic AI World
Traditional DLP programs were designed for predictable, human-driven workflows. Agentic AI operates differently, moving at AI speed and acting autonomously. MIND’s DLP for Agentic AI brings context-aware automation to data security, helping teams prevent risk before it impacts the business.
As organizations continue to invest in agentic AI, MIND positions data security and controls as the missing piece required to achieve AI-driven outcomes safely and sustainably.
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