Series B Brings Total Funding to $112 Million
Seattle-based MIND has raised $72 million in Series B funding, bringing its total capital raised to $112 million. The round was led by Crosspoint Capital Partners, with participation from returning investors YL Ventures and Paladin Capital Group.
MIND builds an AI-native data loss prevention (DLP) platform aimed at closing a gap the company argues legacy DLP tools were never designed to handle: data that moves at the speed AI systems now operate at.
What the Platform Does
MIND's platform provides real-time detection and blocking of data exfiltration attempts across a wide surface area:
- Endpoints
- Generative AI applications
- SaaS deployments
The company says its approach relies on multi-layer classification to identify both content and context before deciding whether an action is a legitimate data flow or an exfiltration attempt. On the response side, MIND uses AI agents to handle investigation, policy tuning, and issue resolution — effectively applying the same agentic-AI pattern to its defensive tooling that attackers are increasingly using offensively.
Why Now
MIND CEO and co-founder Eran Barak framed the funding around a specific pain point security teams are running into as AI adoption accelerates:
"Security leaders are being asked to protect data that moves at AI speed with complex, manual, and incomplete tools designed for a slower world."
That framing lines up with a broader pattern across the industry this year: traditional DLP and data-security tooling built around static rules and periodic scans struggles to keep pace with generative AI applications, autonomous agents, and SaaS-to-SaaS integrations that can move sensitive data in milliseconds rather than the hours or days legacy controls were designed around.
Use of Funds
MIND plans to put the new capital toward:
- Accelerating platform development
- Expanding into key enterprise markets
- Deepening partnership relationships
- Scaling its team
The raise adds to a steady stream of 2026 venture funding flowing into AI-focused data-security and DLP startups, as enterprises look for tooling purpose-built to govern how AI systems — both sanctioned and shadow — touch sensitive data.