MIND Launches Five AI DLP Agents and an MCP Interface to Automate Enterprise Data Security

Model Context Protocol (MCP) Interface

MIND added a Model Context Protocol (MCP) interface that lets security teams direct any of these agents using natural language from any MCP-connected AI client they already use . This means teams can instruct the agents to "investigate last night's PII alerts" or "update the credit-card classifier" in plain language rather than through a separate console .

Reported Customer Metrics

Organizations using the MIND platform reported that the agents reduced incident investigation time from 3–4 hours per incident down to minutes, translating to 25–50% time savings per incident per day, according to Noname Security's CISO Mike Morrato . ThoughtSpot's Senior Director of Security Engineering Al Faiella said the value his team gets is "peace of mind" — incidents arrive with the investigation already done and policies refine themselves .

Market Context: Enterprise AI Governance Challenges

MIND's announcement frames the release against the reality that "GenAI applications, Agentic AI and autonomous workflows create and move data faster than security teams can manually govern it" . Security teams still spend the bulk of their time on manual work (building classifiers, investigating alerts, tuning policies, clearing exceptions) rather than on strategy . MIND CEO Eran Barak stated: "AI has fundamentally changed how data is created and moves, but it hasn't changed how most organizations secure it" .

How This Release Builds on MIND's Earlier DLP for Agentic AI

In January 2026, MIND launched DLP for Agentic AI, a data-centric approach focused on discovering which AI agents are active, detecting what sensitive data they access, and preventing leaks from AI-to-AI interactions . That earlier release provided the foundational visibility and control layer over agentic AI workflows . The July 2026 agents release sits on top of that foundation: whereas the January release gave teams visibility and policy controls for AI agent data access, the new agents automate the operational execution of those policies — building classifiers, investigating incidents, creating policies, and remediating exposures autonomously . Together, the two releases move from "see what AI agents are doing" to "let AI agents run the DLP program itself."