OWASP NHI guidance, OWASP Agentic AI guidance, and the NIST AI Risk Management Framework are the most relevant starting points. For organisations that already manage identities and access, the useful question is whether agent permissions are scoped, reviewed, and enforced as carefully as human or service-account privilege.
Why This Matters for Security Teams
AI agents and MCP connections change the risk profile from passive model use to active execution. Once an agent can call tools, retrieve context, or trigger workflows, the question is no longer only whether the model gives a safe answer. It becomes whether the system can be induced to take unsafe actions, disclose secrets, or chain trust from one tool to another. That is why NHI Management Group treats agent governance as an identity and control problem as much as an AI problem.
The most useful starting points are the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10, because both push teams to define risk, assign ownership, and validate behaviour before deployment. The practical issue is that many organisations still review prompts and model outputs while ignoring the permissions attached to tools, retrieval layers, and runtime connectors. In practice, many security teams encounter agent misuse only after a workflow has already accessed data or executed an action, rather than through intentional privilege design.
How It Works in Practice
Governance for AI agents and MCP should start with a clear inventory of what each agent can do, what it can reach, and what it is allowed to change. That means documenting the agent, its toolset, any MCP server or connector, the identities used at runtime, and the data sources exposed through retrieval. The control objective is simple: scope execution authority as tightly as possible and make privilege review routine, not exceptional.
Practitioners usually break this into four checks:
- Confirm the agent’s purpose and limit it to one or a few approved tasks.
- Separate human approval from machine execution for high-impact actions.
- Log tool calls, context fetches, and downstream actions for traceability.
- Validate that secrets are injected only when needed and never stored in prompts or long-lived state.
This is where the identity bridge becomes important. An agent that uses an API key, service account, or delegated token is effectively operating as a non-human identity, so governance should include entitlement review, rotation, and revocation discipline. The OWASP guidance for OWASP Top 10 for Agentic Applications 2026 is useful for identifying prompt injection, excessive agency, and unsafe tool use, while MITRE ATLAS adversarial AI threat matrix helps teams think about model manipulation and abuse paths. These controls tend to break down when agents are granted broad connector access in fast-moving development environments because ownership, logging, and approval steps are treated as optional friction.
Common Variations and Edge Cases
Tighter agent governance often increases release friction and operational overhead, requiring organisations to balance faster automation against stronger control of execution authority. That tradeoff is especially visible when an agent is embedded in customer-facing workflows, where latency and autonomy are prized, but the blast radius of a mistake is much larger.
There is no universal standard for MCP-specific governance yet, so current guidance suggests combining AI governance with identity controls and secure development practices. For low-risk retrieval use cases, a narrow read-only tool set may be sufficient. For agents that can send emails, move tickets, approve actions, or invoke infrastructure changes, best practice is evolving toward explicit approval gates, bounded scopes, and separate identities for each environment.
Another edge case is third-party or shared MCP infrastructure. If multiple agents or teams reuse the same connector, the environment should be treated as a shared trust boundary, not a convenience layer. That is where NIST Cybersecurity Framework 2.0 remains helpful for mapping governance, protection, detection, and response obligations across the full lifecycle. The CSA MAESTRO agentic AI threat modeling framework is also useful where teams need to model trust boundaries between the agent, the model, and the tools. The hard cases are environments where MCP is stitched into legacy automation, because inherited permissions and undocumented connectors make it difficult to prove what the agent can truly reach.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI Top 10 and MITRE ATLAS address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | Agent and MCP runtimes often rely on non-human credentials and delegated access. | |
| OWASP Agentic AI Top 10 | Directly addresses prompt injection, unsafe tool use, and excessive agency in agents. | |
| NIST AI RMF | GOVERN | AI RMF governance is the anchor for ownership, accountability, and risk treatment. |
| NIST CSF 2.0 | PR.AC-4 | Agent and MCP access should follow least-privilege and controlled entitlement principles. |
| MITRE ATLAS | ATLAS maps adversarial AI tactics that can target agents, prompts, and tool chains. |
Assess agent prompts, tools, and execution paths for abuse before enabling production access.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on August 20, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
