A rising AI-powered cybersecurity startup is tackling one of the industry’s biggest headaches—alert fatigue. MIND’s new DLP tool, unveiled at Black Hat USA 2025, promises faster detection, fewer false positives, and smarter protection for sensitive data across modern enterprise environments.
Key Takeaways
- 91% higher accuracy than legacy DLP tools, reducing false positives.
- Context-aware AI engine that understands sensitive data beyond RegEx patterns.
- Designed for MSSPs & enterprises, scaling security without overwhelming teams.
- Funding momentum—$40M raised, 500% customer growth since late 2024.
- Full coverage for data at rest, in motion, across SaaS, endpoints, and AI tools.
AI Meets a Breaking Point in DLP
In a cybersecurity landscape crowded with big names like Microsoft, Google Cloud, and Zscaler, a two-year-old Seattle startup is making waves. MIND stepped into the spotlight this week at Black Hat USA 2025, unveiling its AI-native data loss prevention (DLP) platform built for speed, accuracy, and adaptability.
The launch addresses a pressing problem: security teams drowning in alerts, chasing false positives, and dealing with outdated classification systems. According to MIND, its platform delivers 91% greater accuracy than traditional DLP solutions, slashing noise and restoring focus to real threats.
Why This Matters Now
Cyber threats are not just increasing—they’re evolving. Attackers are using AI to sharpen their tactics, while enterprises are struggling to keep pace with data sprawl, hybrid work, and the explosion of SaaS and generative AI tools.
“Data loss prevention is at a breaking point,” MIND CEO Eran Barak said. “Legacy tools push security teams into reactive mode. Our AI turns that around—identifying and protecting sensitive data in real time.”
How MIND’s AI DLP Works
At the heart of MIND’s solution is MIND AI, a layered classification engine that goes beyond traditional RegEx matching. Instead of simply looking for patterns, it understands context, classifying sensitive file types with precision.
The platform offers:
- Out-of-the-box policy templates aligned with business needs.
- Automated workflows & threat remediation integration.
- Protection for data at rest and in motion—covering emails, SaaS apps, endpoints, and even generative AI tools.
- Context-aware controls that keep employees compliant without slowing them down.
This is particularly relevant for Managed Security Service Providers (MSSPs), giving them the scale and adaptability needed to handle multiple clients without adding operational strain.
AI’s New Role in Cybersecurity
The move comes as the DLP market shifts toward AI-powered capabilities. Cloudflare, Microsoft, Fortinet, and others have already integrated AI into their data protection tools. The challenge? Balancing automation with trust.
Cloudflare’s own analysis earlier this year summed it up: deterministic detection methods often fail to spot personally identifiable information (PII) or intellectual property (IP), generating false positives that lead to “alert fatigue.”
MIND’s approach? Use generative AI to dynamically learn from user and system behavior—reducing manual classification work, adapting policies in real time, and catching threats without flooding inboxes with false alarms.
Fuelled by Funding and Growth
MIND isn’t just launching products—it’s growing fast. The startup closed a $30 million Series A in June 2025, bringing total funding to $40 million. Since emerging from stealth in October 2024, it has seen 500% customer growth, with some Fortune 1000 companies already onboard.
Research conducted by MIND and the Enterprise Strategy Group earlier this year showed:
- 78% of organizations find DLP tools hard to manage.
- 94% use at least two security tools with DLP capabilities—most use more than three.
- 91% say reducing alert noise is a top priority.
The Road Ahead
Barak believes that AI won’t just be helpful—it will be essential. As non-human identities (bots, autonomous agents) become a normal part of business operations, context-aware AI will be crucial for managing access, spotting anomalies, and preventing breaches at machine speed.
For security teams and MSSPs alike, the pitch is clear: an autonomous DLP that scales with you, adapts in real time, and doesn’t drown you in noise.
“Without AI,” Barak warns, “modern DLP is ineffective. The scale, velocity, and complexity of today’s data are beyond human capability. AI isn’t optional anymore—it’s survival.”
Conclusion
With alert fatigue eroding the effectiveness of security teams, MIND’s AI-driven DLP tool could be a game-changer. If its claims of accuracy and reduced noise hold up in real-world deployments, it might set a new standard for how sensitive data is protected in the AI era.