Topic starter
05/07/2026 6:58 pm
TL;DR: Model Context Protocol standardises how AI models call tools, use memory, and carry context, which can improve traceability and access control but also expands the attack surface for agentic systems, according to TROJ.AI. For identity teams, the real issue is not interoperability itself but whether governance keeps pace with dynamic tool use and auditable context boundaries.
Editorial analysis by NHI Mgmt Group, based on content published by TROJ.AI: “What Is Model Context Protocol (MCP)?”.
Key questions
Q: How should security teams govern MCP servers used by AI coding assistants?
A: Treat MCP servers as privileged trust boundaries, not simple data sources.
Q: Why do enterprise AI agents create governance risk even when MCP is approved?
A: MCP is only one path an agent can use.
Q: What breaks when an AI agent is allowed to call tools without strict scope controls?
A: The main failure is privilege expansion.
Practitioner guidance
- Map MCP trust boundaries Document every MCP server, tool, memory source, and context injector as a governed trust boundary with an owner, policy, and audit trail.
- Separate context from authority Require explicit policy for which data sources can influence an agent's action path, and do not assume retrieved context is safe to act on by default.
- Inventory custom adapters Find every bespoke adapter or mapping used for proprietary or internal APIs and treat it as a security control point with logging and review requirements.
Bottom line: MCP matters because it turns context, tool use, and memory into a governed access layer for agentic AI systems.
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05/07/2026 7:21 pm
MCP is becoming the control plane for agentic identity, not just a transport protocol. The important shift is that access decisions for AI agents increasingly happen at the layer where context, tools, and memory are assembled. That makes the protocol part of the identity stack, because it governs what the agent can know and do in the same session. Practitioners should treat MCP boundaries as policy boundaries, not implementation detail.
A few things that frame the scale:
- According to PwC, 79% of organizations have already adopted AI agents to some degree.
A question worth separating out:
Q: Should organisations standardise on MCP for all AI agents?
A: No. Standardising on MCP for every case can add unnecessary complexity for local developer workflows that do not need multi-user governance. The better approach is to use MCP where the agent acts for someone else and CLI where the task remains tightly bound to a single operator’s environment.
👉 Read our full editorial: MCP changes the control plane for agentic AI identities