MIND
AI-native data loss prevention and insider risk management platform built to automate DLP detection and response with far less manual rule-tuning than legacy tools.
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MIND builds an AI-native data loss prevention and insider risk management platform aimed at automating a category of security tooling that has historically required constant manual tuning. Traditional DLP relies on security teams writing and maintaining large libraries of regex and keyword-matching rules that generate high false-positive rates and still miss novel exfiltration paths; MIND’s pitch is that its proprietary endpoint agent and AI engine can detect and act on sensitive-data exposure with far less manual rule-writing, across endpoints, cloud apps, and increasingly, GenAI tool usage.
Founded in 2023 and headquartered in Seattle, Washington, MIND was started by Eran Barak, who previously founded and sold security automation vendor Hexadite to Microsoft, alongside co-founders with prior roles at Torq, Axonius, and Dazz. The company emerged from stealth in October 2024 with an $11 million seed round led by YL Ventures, then closed a $30 million Series A just seven months later in June 2025, led by Paladin Capital Group and Crosspoint Capital Partners with participation from Okta Ventures, bringing total funding to roughly $41 million.
MIND reports 500 percent customer growth in the seven months following its stealth exit and says it has gained traction among Fortune 1000 companies, protecting sensitive data across hundreds of thousands of endpoints via its agent. Those figures are self-reported rather than independently audited, but the rapid, well-regarded fundraising and the founding team’s track record (a prior successful automation exit to Microsoft) are independently verifiable signals of credibility.
Innovation Matrix Assessment
MIND went from stealth exit in October 2024 to a $30M Series A just seven months later in June 2025, while also reporting rapid feature and customer growth, an unusually fast pace for a DLP platform launch.
The founding team includes Eran Barak, who previously built and sold Hexadite to Microsoft, and the company reports Fortune 1000 traction and protection across hundreds of thousands of endpoints, though these operational figures are self-reported.
Roughly $41M raised across two rounds in under a year, backed by Paladin Capital, Crosspoint Capital, YL Ventures, and Okta Ventures, is a strong and fast capital trajectory for a two-year-old company.
Replacing manually-tuned regex and keyword DLP rulesets with an AI engine aimed at reducing false positives and covering GenAI-era exfiltration paths is a real architectural shift from incumbent DLP vendors like Symantec and Forcepoint.
The company's claimed 500% customer growth and 'hundreds of thousands of endpoints protected' are vendor-reported figures without independent audits, named customer case studies, or third-party test results found in this research.
Data loss prevention and insider risk are top-of-mind for CISOs as GenAI tool adoption creates new, harder-to-monitor data exfiltration paths, making an AI-native approach to this specific problem timely.
Why CISOs Should Care
Targets the chronic pain point of legacy DLP, constant rule tuning and high false-positive rates, with an AI engine intended to cut manual overhead while extending coverage to GenAI-era data risks.
What Makes It Different
Built AI-native from the start around automated detection and response rather than retrofitting AI onto a rule-based DLP engine, with a founding team that has a prior successful security-automation exit.
The Matrix Verdict
70/100 — MEANINGFUL INNOVATOR
A fast-moving, well-funded, credibly-founded DLP challenger with real momentum; the underlying efficacy claims are still vendor-sourced and would benefit from independent validation as the company matures.
Editorial Note: Claims vs. Verified Findings
Funding amounts, investors, and founder backgrounds (including the Hexadite/Microsoft exit) are independently reported by GeekWire, SecurityWeek, and Paladin Capital Group. Customer growth percentage and 'hundreds of thousands of endpoints' figures are self-reported by MIND and not independently corroborated in this research.
Sources
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