Topic starter
12/07/2026 12:26 pm
TL;DR: AI agents are now calling production APIs, querying databases, and using MCP with more standing access than many humans, and Pomerium’s comparison of six platforms shows the market splitting between runtime enforcement and NHI discovery. Identity controls built for login events do not govern autonomous tool use unless every request is checked in line.
Editorial analysis by NHI Mgmt Group, based on content published by Pomerium: “IAM for Agentic AI: 6 Platforms Compared”.
By the numbers:
- At least 1,862 internet-exposed MCP servers were found running with zero authentication, creating a large unauthenticated attack surface behind agent tool calls.
Key questions
Q: What breaks when an AI agent is allowed to call tools without strict scope controls?
A: The main failure is privilege expansion.
Q: Why do AI agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
Q: How do teams know whether they need NHI discovery or runtime enforcement for agentic AI?
A: If you cannot tell how many service accounts, secrets, or shadow agents exist, discovery is the first problem.
Practitioner guidance
- Map your agentic access boundary Identify where AI agents, workloads, and MCP tools intersect in production and decide whether each boundary needs inline enforcement or inventory governance first.
- Treat MCP tools as separate policy objects Assign different access decisions to read, write, admin, and destructive methods instead of granting a single server-wide permission.
- Replace durable agent secrets with request-bound identity Use short-lived assertions or brokering patterns so the agent runtime never stores reusable credentials that can be exfiltrated and replayed.
Bottom line: Agentic AI breaks IAM assumptions that were built around human login events and stable session pacing.
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12/07/2026 12:39 pm
Login-era IAM assumptions collapse when agents can initiate production work continuously. IAM for humans was built around a stable user session that starts with authentication and then unfolds inside bounded workflows. Agentic systems break that timing model because access is exercised at the moment of each tool call, not only at login. The implication is that identity governance for agentic AI has to move from session establishment to request-time enforcement.
A few things that frame the scale:
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between MCP server access and tool-level authorisation?
A: Server access answers whether a user can enter the MCP boundary at all, while tool-level authorisation answers which operations are permitted inside that boundary. Enterprises need both because coarse access without fine-grained enforcement leaves too much power attached to a single session.
👉 Read our full editorial: IAM for agentic AI exposes the limits of human-era controls