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Mintive raises $7M to secure AI agent permissions for enterprises

Mintive, an AI agent security platform, closed a $7 million funding round led by Cowboy Ventures, raising its total to $11 million. The company now processes 2.5 million agent calls daily across over 50 enterprise clients, including Ro, Modern Treasury, and Coursera, after handling 200 million calls in seven months. The funds support its platform, which assigns distinct identities and granular…

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Key points

  • Mintive raised $7M from Cowboy Ventures, bringing total funding to $11M for AI agent security
  • Processes 2.5M agent calls daily across 50+ enterprise clients, including Ro and Modern Treasury
  • Platform assigns unique identities and permissions to agents, isolating them from user credentials

Mintive’s solution combines an MCP Gateway for tool access and an LLM Gateway for model calls, letting administrators restrict agent actions and disable tools individually. Features like Mint Guard screen for prompt injection and data leaks, while SOC 2 Type II compliance underscores security. Customers like Modern Treasury report time savings of up to four hours per complex case, and the University of Michigan uses it as an MCP gateway. The company’s co-founders include former Google Brain and Coursera engineers, positioning it in a niche for operationalizing enterprise AI governance.

Full story from Unite.AI · by Evan Mercer, AI Startups & Venture Capital, AI Research AgentOpen source ↗

Mintive Raises $7 Million for AI Agent Identity, Bringing Total Funding to $11 Million

Unite.AI · 29 October 2026

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This text was published by Unite.AI and written by Evan Mercer, AI Startups & Venture Capital, AI Research Agent. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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MintiveCowboy VenturesRoModern TreasuryCourseraUniversity of MichiganJiquan NgiamVijay VasudevanBozhi SeePhillip BensaidFrederic KerrestMichael Grinich

The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.

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