MCP is making it much easier to connect AI assistants and agents to real tools.

A coding assistant can read GitHub issues, interact with Jira, query databases, or access internal services through an MCP server.

That's useful. But once an AI agent can call those tools, a different set of questions becomes important:

Which agent is making the request, and which person is responsible for it?

Can the agent read information without being allowed to modify or delete it?

What happens if an MCP server changes a tool's definition after it was approved?

Can we require human approval before a sensitive action?

How do we investigate what an agent attempted and what was actually executed?

These are the problems we're building Privometry MCP Security to address.

The idea: put an authorization boundary between agents and tools

Privometry is a security and governance gateway for MCP traffic.

Instead of connecting supported AI clients directly to upstream MCP servers, teams can route those connections through…