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MIND
Seattle data loss prevention company, with a large share of its staff in Israel, that discovers and blocks sensitive data across SaaS, generative AI tools, AI agents, endpoints, email, and file shares.
Security teams that need to see sensitive data moving into AI tools and agents, and to block it, without standing up a separate project for every channel.
Shops that only want an endpoint agent with no AI-tool coverage, or buyers who cannot tolerate a policy that blocks a paste.
Verdict
MIND is a data loss prevention company based in Seattle, with 40 of its roughly 70 employees in Israel. The platform discovers and classifies sensitive data, detects risky activity, and can block it across SaaS, generative AI tools, AI agents, endpoints, on-premises file shares, and email. Security teams use it, and the MIND AI DLP Agents, to investigate exposures and to stop data from entering AI tools. Security teams use the platform when employees and agents move corporate data into AI systems that older prevention tools were not watching.
On September 17, 2026, MIND announced a $72 million Series B led by Crosspoint Capital Partners, with YL Ventures and Paladin Capital Group participating. The round brings total funding to $112 million, a year after a $30 million Series A. Company sources told CTech the round values MIND at $300 million, and CTech reported a $10 million annual revenue run rate, about 18 months after the company started selling (CTech). Crosspoint's check was about $35 million. MIND says revenue grew more than 17 times and customer count more than 8 times in the past year, that it has analyzed billions of events and stopped data loss across hundreds of thousands of endpoints, and that it was the first data security company accepted into Anthropic's Cyber Verification Program and the first to earn ISO/IEC 42001 (MIND). CEO Eran Barak co-founded the company in 2023 with CTO Itai Schwartz and VP of R&D Hod Bin-Noon. Named customers on the funding announcement include Children's Hospital Los Angeles and the National Geographic Society (MIND).
Score Breakdown
How MIND scores in the categories that matter to its buyers.
Pricing
MIND sells an enterprise data loss prevention platform. CTech reported a $10 million annual revenue run rate (CTech).
The Field at a Glance
Where MIND ranks among Security, safety & guardrails vendors we reviewed, by Overall Score and relative typical engagement cost.
MIND scores 7.4 in this peer set, above Lakera (6.8) and below Noma Security (8.0) and HiddenLayer (8.1). Relative cost sits with enterprise security platforms sold to a security team.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Block sensitive data from entering AI tools | Strong | Children's Hospital Los Angeles described this on endpoints. |
| See where data meets AI agents | Strong | National Geographic Society cited that visibility. |
| Day-to-day DLP investigation | Strong | MIND AI DLP Agents take on that work. |
| Email, SaaS, endpoints, and file shares together | Strong | The platform lists all of those surfaces. |
| A hands-off install with no blocking | Weak | Policies can automatically block risky activity. |
Who it’s for
Good fit
- Security teams adopting generative AI and agents who still have to keep regulated data in bounds
- Hospitals and other organizations that worry about protected health information in AI tools
- Programs that want classification, detection, and blocking in one place
Poor fit
- Teams that only want a report and never a block
- Buyers who need a decade of DLP displacement stories before a pilot
- Shops that will not put an agent on endpoints
Review Excerpts
Below are excerpts from public reviews and coverage. Paid reviews and pay-for-play sites such as Clutch were excluded.
“MIND delivers on 'Stress Free DLP'. We use MIND to identify and label sensitive documents and restrict public sharing. It's giving us something we didn't have before, which is visibility into where and when our data interacts with AI agents. It's become a key part of how we protect data across our organization.”
“As we expand our staff's use of AI, protecting patient information without slowing innovation is essential. MIND lets us discover and block protected health information from entering AI tools across our endpoints without any performance impact.”
“MIND blocked a paste into the AI assistant because the policy treated the text as sensitive. I wanted that block on patient data, and I also lost the morning note I was trying to send.”
Methodology
This page is an independent evaluation of MIND for buyers comparing options in security, safety & guardrails. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. MIND did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria fall under it in two groups.
- Buyer outcomes (for security, safety & guardrails)
- Coverage across AI tools and endpoints
- Classification of content and context
- Agents that run day-to-day DLP work
- Named customer evidence
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites such as Clutch are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the fit pattern described above.
Scope
MIND is graded here as security, safety & guardrails. Criteria scores can move as more review volume and product checks are added.