MIND, the first data security platform to enable autonomous Data Loss Prevention (DLP) and Insider Risk Management (IRM) programs, announced the MIND Autonomous DLP Analyst. This DLP breakthrough is designed to help security teams automate the operational work required to run modern data security programs with pre-built skills: Custom Classifier and Issue Investigator.

Operating a DLP program has traditionally required extensive manual effort. Security teams must define what sensitive data looks like for their organization and then investigate alerts across multiple systems to determine whether real risk exists. These workflows are time consuming, difficult to scale and often slow teams down from addressing the incidents that matter most.

"Security teams should not have to write endless regex rules or manually conduct investigations to protect their data," said Eran Barak, Co-Founder and CEO at MIND. "This is just the beginning of how the MIND Autonomous DLP Analyst will take on complicated data security tasks so our customers can mind what matters."

Since becoming the first data security company to achieve ISO 42001, MIND will continue to build additional AI skills into the Autonomous DLP Analyst to achieve its vision of stress-free DLP.

Autonomously customize business-specific data classification

Every organization has sensitive data that is unique to its business. This includes proprietary formulas, source code, internal strategy documents and operational records that do not follow predictable patterns.

Traditionally, security teams rely on complex regex rules, static policies and continuous tuning to classify this information. This approach is difficult to maintain and often produces false positives that consume valuable analyst time.

The Custom Classifier skill allows organizations to teach the platform what sensitive data looks like for their specific environment. Security teams can upload representative examples of business-specific data elements. The Custom Classifier analyzes these examples to automatically generate precise classifiers and uploads them to MIND's proprietary multi-layer AI classification engine.

These classifiers are deployed within the customer's private MIND instance and operate across their entire environment including SaaS and GenAI apps, agentic AI systems, on-premise file shares, endpoints and emails.

Accelerate data security incident investigations

Security teams spend significant time sorting out signals from noise and reconstructing activity across systems. When alerts occur, analysts often need to manually piece together user actions, file activity and data sensitivity to understand what truly is at risk.

The Issue Investigator skill provides automated issue analysis that highlights key risk signals and guides security teams through the investigation process. Instead of manually reconstructing events, security teams receive clear context that helps them quickly understand what happened and recommend the appropriate response.

The result is faster triage, clearer context and more confident decision making to quickly resolve data security issues.

Advancing the vision of autonomous data security

The Autonomous DLP Analyst represents a bold step towards effective DLP operations. By applying AI and autonomy directly to the operational workflows of data security programs, MIND helps organizations reduce manual effort while improving their ability to identify and respond to real risk.

"Every organization has sensitive data that is unique to its business," said Tom Mayblum, VP of Product at MIND. "The Autonomous DLP Analyst allows security teams to teach the platform what matters most in their environment and quickly understand potential data security issues."

The Autonomous DLP Analyst builds on MIND's AI native architecture and advances their vision of autonomous data security. With this release, MIND helps organizations protect sensitive information across modern environments without increasing complexity or headcount.