AI Governance & Security Vendor Wiki

5 min read

MIND

Researched 2026-06-28. Primary category: dlp.

One-liner — An AI-native, “autonomous” data-loss-prevention (DLP) and data-detection-and-response (DDR) platform that discovers, classifies, and blocks sensitive-data leaks across endpoints, SaaS, email, on-prem file shares, and GenAI tools.

Categories — dlp

What it does

MIND is a next-generation DLP / insider-risk platform pitched as putting DLP and insider-risk-management programs “on autopilot.” It continuously discovers and classifies sensitive data wherever it lives or moves, then detects risky data movement in real time and prevents exfiltration. The selling point versus legacy DLP (Symantec/Forcepoint-era tooling) is automation and accuracy: a multi-layer AI classification engine that goes beyond RegEx pattern matching, with the explicit promise of cutting the false-positive noise and analyst toil that made legacy DLP notoriously painful to run. Vendor-cited customer numbers (marketing) claim ~80% less DLP management time and ~95% fewer false positives.

A distinguishing angle is GenAI coverage: MIND treats GenAI interactions as a live data source, classifying each prompt, upload, and paste at the moment of risk to stop sensitive content (PHI, CUI, credentials) from going into tools like ChatGPT or Copilot.

Where it sits in the stack

Data layer — dlp. MIND blocks exfiltration (stopping sensitive data from leaving via uploads, pastes, email, or GenAI prompts) and is anchored in sensitive-data discovery + classification (knowing what’s worth protecting). It lives at the boundary between the green/trusted zone and untrusted external destinations. Functionally adjacent to DSPM (data-at-rest posture) and to AI-access-governance / CASB-for-AI, since its GenAI-prompt inspection overlaps with shadow-AI controls.

Deployment & architecture

  • SaaS control plane with a lightweight endpoint agent and a browser extension. The agent maintains a real-time inventory of local files, browser activity, and app data; the browser extension is what enables prompt-to-response GenAI inspection.
  • Coverage surfaces: GenAI tools, SaaS apps, endpoints, email, and on-prem file shares — pitched as eliminating the silos of separate legacy DLP products.
  • Remediation: automated responses, guided end-user workflows, and integration with existing remediation platforms.
  • Procurement: transactable on AWS Marketplace (usable against existing AWS spend).
  • SIEM/SOC, IdP, and DSPM integration specifics were not confirmed from primary sources — see open questions.

Positioning & differentiators

Positioned as “the first autonomous DLP platform,” merging data discovery, AI classification, policy management, and automated prevention into one tool rather than the stitched-together legacy DLP stack. Founder pedigree is a real differentiator for credibility: CEO Eran Barak founded Hexadite (acquired by Microsoft, 2017); CTO Itai Schwartz was an early employee at Torq and Axonius; VP R&D Hod Bin Noon was first employee at Dazz — all ex-Unit 8200. Nearest neighbors: cyberhaven (data-lineage DLP + shadow-AI), nightfall-ai (API/SaaS AI-based DLP), forcepoint and microsoft-purview (incumbent DLP), and on the GenAI-prompt side prompt-security and lasso-security. On data discovery it brushes against DSPM players cyera, sentra, bigid.

Ownership, funding & M&A

Independent, VC-backed. Emerged from stealth October 2024 with an $11M seed led by YL Ventures. Raised a $30M Series A on 2025-06-04, led by Paladin Capital Group and Crosspoint Capital Partners, with Okta Ventures and existing investor YL Ventures participating; total funding >$40M. No acquisition — seed registry carried no M&A flag, and none found. Ownership confidence: high (independent), funding confidence: medium (press-reported, consistent across sources).

CTO / hedge-fund lens

DLP is a Day-1 control for a regulated asset manager: preventing exfiltration of MNPI, position data, PII, and source code is table stakes, and GenAI usage has reopened the egress problem that legacy DLP handled poorly. MIND’s appeal to a lean shop is the automation story — DLP’s historical cost is the analyst headcount needed to tune rules and chase false positives, and MIND targets exactly that. No direct SR 11-7 / model-risk role (it protects data, it is not a model). Caution flags for a hedge-fund CTO: it is an early-stage (2023, Series A) vendor, so reference-checking maturity, scale, and the accuracy claims matters; and there is meaningful overlap with whatever DSPM, CASB, or email-DLP the shop already runs. A fit for a fund that wants a single modern DLP+GenAI-egress tool rather than assembling Purview/Netskope/point products — but verify it can stand in for, not just add to, existing controls.

Competitors / alternatives

cyberhaven, nightfall-ai, forcepoint, microsoft-purview, netskope, prompt-security, lasso-security; DSPM-adjacent: cyera, sentra, bigid.

Open questions / to verify

  • SIEM/SOC, IdP (SSO/SCIM), MCP, and DSPM integration specifics — not confirmed from primary sources.
  • Independent (non-vendor) validation of the accuracy / false-positive claims.
  • Whether on-prem/self-hosted deployment exists or it is SaaS-only (control plane appears SaaS).
  • Customer count / scale and named enterprise references.

Sources

History

  • [2026-06-28] Stub created from seed registry.
  • [2026-06-28] Researched; established the correct company (MIND, mind.io, Seattle-HQ Israeli-founded DLP startup, founded 2023 by ex-Unit 8200 / ex-Hexadite team). Confirmed independent, $11M seed (YL Ventures, Oct 2024) + $30M Series A (Paladin + Crosspoint, 2025-06-04), >$40M total. Documented autonomous DLP/DDR + GenAI-egress positioning and endpoint-agent/browser-extension architecture. Set ownership_confidence high (independent), status researched, 3 sources cached.

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