Why do AI agents and MCP tool calls increase the need for centralized identity and access control?

Why This Matters for Security Teams

AI agents change the access model because a single request can trigger multiple tool calls, each with its own scope, side effects, and data exposure. Centralised identity and access control is no longer just an administrative preference; it is what keeps agent actions attributable, reviewable, and limited to approved intent. That matters when an agent can read, write, escalate, or retrieve secrets across SaaS, cloud, and internal systems.

This is especially important for MCP-based integrations, where the protocol can make tool discovery and invocation easier, but does not by itself solve authorisation, consent, or provenance. Current guidance from the NIST AI Risk Management Framework and agentic AI research both point to the same operational problem: without a central policy layer, permissions drift into the connector, the prompt, or the model wrapper, where they are difficult to govern consistently. In practice, many security teams encounter excessive agent access only after a tool has already been used in an unintended workflow.

How It Works in Practice

Centralised identity and access control works by treating the agent, the human requester, and the tool session as distinct but linked identities. The agent should not inherit broad standing access just because it can call tools. Instead, each action should be evaluated against policy using the requester’s identity, the agent’s workload identity, the resource sensitivity, and the current context. That makes it possible to apply least privilege, step-up approval, and session scoping without guessing later who initiated the action.

In a mature design, MCP tool calls are brokered through an access decision point that enforces permissions before execution and records the outcome for audit. Identity signals should be normalised across systems so that the same user, service, or agent is recognised consistently whether it is accessing tickets, code repositories, databases, or secrets stores. This is where NHI governance becomes relevant: many agents operate like non-human identities with delegated authority, so they need lifecycle control, rotation, and revocation just like service accounts. The OWASP Top 10 for Agentic Applications 2026 is useful here because it highlights tool misuse, excessive agency, and weak control of autonomous execution.

• Bind each tool invocation to a specific user, workload, and approved purpose.
• Use policy engines to enforce scope, time limits, and environment-based restrictions.
• Separate read, write, and escalation permissions rather than granting a single broad agent role.
• Log prompt, decision, tool selection, and output handling as one traceable transaction.
• Review agent entitlements on the same cadence as privileged service accounts and other NHIs.

Where teams need threat modelling for agent misuse and model-mediated attacks, the CSA MAESTRO agentic AI threat modeling framework and the MITRE ATLAS adversarial AI threat matrix provide a stronger starting point than generic access reviews alone. These controls tend to break down when agents are allowed to chain tools across loosely governed SaaS integrations because identity context is lost at each hop.

Common Variations and Edge Cases

Tighter central control often increases integration overhead and can slow experimentation, so organisations have to balance safety against the need for rapid agent development. Best practice is still evolving for delegated consent, transient agent sessions, and human-in-the-loop approval thresholds, and there is no universal standard for this yet.

One common edge case is a hybrid environment where some tools are internal and strongly governed while others are third-party MCP servers with limited policy hooks. Another is ephemeral agent execution, where the agent exists only for a short task and may not fit traditional IAM lifecycle processes. In those cases, the practical answer is to anchor controls in the identity provider, secrets manager, and orchestration layer rather than in the tool itself. The OWASP Non-Human Identity Top 10 is particularly relevant when agents hold credentials or tokens that outlive a single run, while NIST SP 800-53 Rev 5 Security and Privacy Controls remains the most practical reference for access enforcement, logging, and accountability controls.

For regulated payment environments, the same centralisation logic supports stronger evidence of restricted access and traceable authorisation. That matters when agents can touch customer data, payment workflows, or administrative functions. The PCI DSS v4.0 expectations are easier to demonstrate when agent permissions are explicit, reviewed, and revocable rather than embedded in scattered connectors.

Related resources from NHI Mgmt Group

• Why do MCP environments increase identity governance complexity for AI agents?
• Why do AI agents increase identity and access risk in SOC workflows?
• Why do large MCP tool catalogs increase risk for AI agents?
• Why do AI tools and agents increase the importance of data visibility and access control?