MIND sells subscription software that helps enterprise security teams find sensitive information, understand who can reach it and stop it from leaving through business applications, employee devices, email or AI tools. Dealroom reports that the company has raised a $72 million Series B to expand that data-loss-prevention business.
A customer connects MIND to software services, endpoints, on-premises file shares and email systems. The platform discovers and classifies unstructured data, evaluates activity and user behavior, and assigns risk to potential exposure or exfiltration incidents. It can then revoke access, delete or relabel a file, alert its owner, coach the user, block the action or escalate the decision while preserving an audit trail.
MIND’s pitch is that data visibility and enforcement should operate as one workflow. The company says its software examines both stored information and data moving between users and applications, then creates and applies policies according to content and business context. That approach is intended to replace static rules and manual alert triage without removing security teams from consequential decisions.
The customer is an information-security team responsible for protecting company and customer records. MIND’s terms describe remote software access sold for subscription fees, while its customer materials identify Guild, ThoughtSpot, OpenWeb, Noname Security and Gravity Payments among its users. The same materials describe deployments spanning technology, healthcare, financial services and other industries.
Crosspoint Capital Partners led the Series B, with existing investors Paladin Capital Group and YL Ventures participating, according to Dealroom. The outlet says MIND plans to use the financing for product development, expansion in major enterprise markets, technology and channel partnerships, and hiring. It also cites MIND’s claim of more than 1,700% revenue growth and 800% customer growth over the past year.
The financing gives MIND more capacity to extend the platform across enterprise systems, but the operating test remains specific: its classifications and automated interventions must stay accurate as coverage expands. If they do not, the product risks recreating the alert noise and workflow disruption that customers are buying it to reduce.