MIND is an AI-native security platform automating data loss prevention (DLP) and insider risk management across SaaS, Gen AI tools, endpoints, and on-premises systems. The stack reveals a distributed-systems-first engineering culture: Kubernetes, ArgoCD, Flux, and Terraform dominate infrastructure; Python, TypeScript, Go, and C# power backend services; real-time observability is baked in via OpenTelemetry and Grafana. Hiring is heavily engineering-skewed (2 engineering roles active) with senior talent concentrated there—consistent with a young company (founded 2023) prioritizing product velocity over breadth.
MIND builds an AI-native platform that automates data loss prevention and insider risk management for enterprises. The product ingests data events across SaaS applications, Gen AI tools, endpoints, on-premises file shares, and email systems; classifies sensitive data in real-time; and executes automated remediation actions to stop data leaks. Headquartered in Seattle and founded by cybersecurity industry veterans, MIND targets mid-to-large organizations working to operationalize DLP and IRM programs at scale. Current hiring focuses on engineering and product roles, with secondary effort in marketing and field demand generation.
MIND's infrastructure is built on GCP, AWS, and Azure with Kubernetes orchestration (ArgoCD, Flux). Backend services use Python, TypeScript, Go, and C#. Real-time telemetry runs via OpenTelemetry and Grafana. Frontend is React with Vite and Vercel.
MIND is based in Seattle, WA. Active hiring spans engineering, product, marketing, and HR roles across the United States and Israel, with emphasis on senior-level engineering positions.
MIND offers AI-driven data loss prevention (DLP) and insider risk management (IRM). The platform detects sensitive data across SaaS, Gen AI tools, endpoints, and email; assesses risk in real-time; and automates remediation actions to prevent data leaks.
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MIND's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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