MCP server security gateway and runtime audit layer for enterprise AI tooling deployments
A sandboxed execution and approval layer that validates, scans, and gates every Model Context Protocol server before it connects to internal AI agents, with per-server budgets, action logging, and rollback controls.
The problem
Enterprise teams deploying internal AI agents with Copilot or Claude need to whitelist and audit third-party MCP servers, but lack a security architecture to validate server behavior, scope permissions per agent, and block malicious or misconfigured servers before they execute. Today teams either block all external servers (crippling agent capability) or deploy servers without security controls (exposing data, compute, and integrations to compromise).
Who has it: Mid-market and enterprise AI platform teams (50–500 engineers) deploying internal agent infrastructure with external tool integrations, especially in financial services, SaaS, and government contractors where data and integration risk is high.
Why now: MCP adoption is accelerating as Claude and Copilot integrations deepen; enterprises are moving beyond demo agents to production deployments with real integrations (databases, APIs, payment systems); security and compliance teams lack native controls for agent-orchestrated tool access, creating a gap between agent velocity and risk governance.
Where this came from
2 public sources behind this idea.
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