MIND’s AI-native platform unifies Data Loss Prevention (DLP) and Insider Risk Management (IRM) with a shared AI classification layer to improve accuracy and reduce false positives, while its lightweight architecture enables rapid time-to-value and lower alert volumes.

The system combines EDM, RegEx, NER, OCR, statistical methods, vector similarity, small language models, and large language models to classify sensitive data across SaaS, endpoints, on‑premises file shares, and email.

Analysts and MIND executives stress proactive, automated protection and a strong ongoing commitment to customer experience, innovation, and market responsiveness.

Frost & Sullivan notes MIND’s roadmap aligns with customer pain points and emphasizes rapid capability development, especially around GenAI risk controls.

MIND credits AI-native innovation, customer-centric development, and active engagement with CISOs and design partners for growth and leadership, with a scalable global adoption strategy.

The growth strategy focuses on AI-native innovations and close collaboration with customers and security leaders to drive widespread, scalable adoption.

MIND positions itself as an autonomous data security pioneer, aiming to discover, classify, fix data security issues, and stop data leaks, with headquarters in Seattle, WA.

A customer-first culture underpins continuous innovation and a commitment to helping organizations protect sensitive data while enabling innovation.

MIND received the 2026 Global New Product Innovation Recognition in the DLP industry from Frost & Sullivan, acknowledging leadership in AI-native data security and customer-centric innovation amid evolving work and AI trends.

GenAI-related controls were introduced to protect sensitive data in GenAI workflows, including policy management, user activity visibility, access controls for enterprise-approved GenAI apps, and prevention of sensitive prompts.

The company emphasizes a lightweight architecture that supports rapid time-to-value and a lower total cost of ownership.

Founded in 2023 in Seattle, MIND was created to address legacy DLP shortcomings such as alert fatigue, high false positives, and limited visibility into unstructured data.