A protected Model Context Protocol server that gives AI agents distinct machine identities, enforces least-privilege OAuth permissions, applies contextual authorization policies, and pauses sensitive actions for human approval.
An enterprise infrastructure layer for the Model Context Protocol that provides authentication, RBAC, audit logging, and rate limiting for tool calls. It acts as a secure proxy between AI agents and MCP servers to ensure security and compliance in production environments.
Security, cost, and health governance proxy for MCP infrastructure. Enforces YAML-configurable security policies (blocklists, rate limits, token budgets), tracks real token costs via tiktoken, monitors server health with live JSON-RPC probes. Features OAuth 2.1/OIDC with RBAC, web dashboard, payload normalization, semantic shell AST analysis, mTLS, and a formal STRIDE threat model.
A zero-trust gateway for securely brokering interactions between AI models and internal tools via the Model Context Protocol, with DLP, prompt injection defense, and LLM-as-a-Judge.
Enables secure MCP traffic by filtering threats with a 3-stage detection pipeline, applying RBAC policies, and redacting sensitive data in real-time via an SSE-inspecting proxy.
