MIND Raises $72M Series B to Scale AI-Native Data Loss Prevention Across Enterprise Stacks

Data loss prevention startup MIND announced a $72 million Series B funding round led by Crosspoint Capital Partners, with continued participation from YL Ventures and Paladin Capital Group. The capital injection brings MIND's total funding to $112 million and comes just over a year after its Series A. The Seattle-based company plans to utilize the fresh capital to accelerate product development, expand team headcount, broaden go-to-market efforts across enterprise segments, and deepen technology integrations. Traditional Data Loss Prevention (DLP) frameworks rely heavily on static regular expressions, brittle rule tables, and perimeter inspection points that were never architected for dynamic generative AI workloads or autonomous software agents. As developers and enterprise staff increasingly funnel unstructured data, source code, and internal telemetry into cloud-hosted large language models (LLMs) and multi-tenant SaaS environments, security teams face a widening visibility gap. MIND addresses this operational blind spot by delivering an AI-native data security platform that applies multi-layer classification to understand data context in real time, automatically deploying intelligent agents to triage alerts, tune security policies, and intercept data exfiltration across endpoints, email, SaaS, and gen-AI interfaces. This funding round mirrors a broader shift across the cloud and AI security landscape, where venture investment is rapidly concentrating into systems purpose-built for the AI era. Legacy DLP platforms generate overwhelming volumes of false-positive alerts, bogging down DevSecOps pipelines and frustrating operational engineering teams. The emergence of agentic workflows and automated policy enforcement highlights how enterprise risk management is transitioning away from manual security reviews toward autonomous runtime protection layers. For DevOps, platform engineers, and SecOps practitioners, the takeaway is clear: embedding data boundaries must move upstream into development and continuous delivery pipelines without introducing developer friction. Teams evaluating AI tools or building agentic frameworks should prioritize solutions that analyze contextual intent rather than merely matching strict patterns. The core trade-off remains balancing low-latency developer velocity against rigorous automated policy tuning to prevent sensitive intellectual property and credentials from leaking into external model training sets or unvetted cloud endpoints.