Runtime authority for AI agents: where access control actually lands

At a glance

What this is: This article argues that AI agent security needs a runtime enforcement layer that decides each action before it executes, rather than only discovering risk, routing traffic, or permissioning tools.

Why it matters: For IAM and PAM teams, the key issue is that agentic systems can act at runtime, so governance built only around visibility or static permissions leaves the real access decision unresolved.

👉 Read Akeyless's analysis of runtime authority for AI agent access control

Context

AI agent governance is splitting into distinct layers, and that separation matters for identity security. Discovery, gateways, workforce governance, and orchestration all contribute something useful, but none of them alone removes standing credentials or evaluates whether a specific action should execute against a production system.

For identity teams, the primary challenge is no longer simply who can log in or which tool is approved. The question is what happens at runtime when an AI agent chooses an action, reaches for a tool, and attempts to use access that may already exist outside any meaningful review window.

Key questions

Q: How should security teams govern AI agents that can access enterprise systems?

A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring. The control set should include inventory, task-bound credentials, audit trails, and revocation paths. If an agent can call tools or touch production systems, it belongs in the same governance model as service accounts and other machine identities.

Q: Why are AI discovery tools not enough for agent governance?

A: Because discovery tells you what exists and how risky it looks, but it does not remove the credential that makes the action possible. A security team can still be left with standing OAuth grants, API keys, or connector secrets even after a platform identifies the agent as high risk.

Q: What do IAM teams get wrong about MCP gateways and AI access control?

A: They often treat gateway rules as equivalent to authorisation, when they are really traffic and tool reach controls. A gateway can limit where requests go, but it does not necessarily eliminate credentials or judge whether the specific action matches the intended purpose.

Q: Who should own AI agent access decisions and lifecycle controls?

A: AI agent access decisions should be owned by the team that deploys and operates the agent, with identity governance and security functions enforcing policy and review. Ownership must be explicit because autonomous behaviour creates accountability gaps if nobody is responsible for the agent's permissions, monitoring, and offboarding.

Technical breakdown

Why AI discovery platforms do not enforce action-level access

AI discovery platforms inventory agents, score risk, and surface shadow usage patterns, but they operate after the fact. They can show which agents exist, what they touch, and where over-permissioned access appears, yet they do not sit in the access path. That means the standing credential, OAuth grant, or connector secret remains usable even when the platform flags it as risky. In IAM terms, these tools improve visibility and triage, but they do not remove the privilege that makes the action possible.

Practical implication: treat discovery as a detection layer and pair it with a control that can stop the action before execution.

How MCP gateways differ from runtime authority

MCP gateways govern traffic and tool reach at a choke point, usually with routing rules, catalogs, and time-bound access. That is not the same as eliminating credentials or understanding the semantic intent of a request. A gateway can hide the key behind the connection and restrict where traffic goes, but it still relies on a standing secret and rule-based access. Runtime authority is positioned one step deeper: it brokers the request, mints credentials just in time, and validates the action before the secret exists in usable form.

Practical implication: if a gateway is your only control, verify whether credentials still exist outside the decision moment.

Why workforce AI governance is not enough for production access

Workforce AI governance platforms help organisations discover employee AI use, assign identities, and apply role-based permissions to approved tools. That model is useful for adoption control, but it does not govern the action itself once the tool is in use. An approved agent can still take an unsafe step inside an approved connector, because role permission is not the same as intent validation. For IAM teams, this is the classic gap between access to a tool and authorised use of the function that tool exposes.

Practical implication: do not equate tool approval with control over the downstream action.

NHI Mgmt Group analysis

Action-level enforcement is the missing identity control for AI agents. Discovery and routing tools reduce blind spots, but they do not answer the identity question that matters most: should this specific action happen at all. Once an agent can select actions at runtime, static permissioning stops being sufficient as the primary control. The implication is that agent governance must move from catalogue and oversight thinking to decision-path thinking.

Standing credential elimination is now a governance boundary, not just a hardening detail. If a credential still exists long enough to be stolen, reused, or abused, the control model still depends on after-the-fact response. Runtime authority changes the category by treating credential existence as part of the access decision itself. Practitioners should read that as a shift in the boundary of privileged access governance.

Per-action intent validation is the new named concept that matters here. This is the point at which a request is tested against what the human or upstream workflow actually meant, not just what an approved tool is allowed to do. That concept matters because AI agents can chain tools, change context, and reach production systems faster than review cycles can react. Security teams need to recognise that intent is becoming an access property, not just a policy concern.

Agent security stacks will remain complementary, but only one layer can remove risk at the moment it materialises. Discovery tells you what exists, gateways govern what may flow, workforce platforms organise who may use what, and orchestration platforms build the workflows. None of those alone replaces a control that sits directly in the access path and decides whether execution should occur. The practitioner conclusion is straightforward: map each product to its actual control layer before assuming governance coverage.

Existing PAM assumptions still apply, but the execution model is changing underneath them. Privileged access, auditability, and least privilege remain the right governance goals, yet AI agents compress the time between request and action. That means IAM teams must judge controls by whether they govern the moment of execution, not just the identity that initiated it. The result is a more precise privileged access model for autonomous systems.

From our research:

• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
• 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
• For a broader governance lens, see OWASP Top 10 for Agentic Applications 2026 for the control patterns most often discussed around agent misuse and tool abuse.

What this signals

With 80% of organisations already reporting agent actions beyond intended scope, the operational signal is clear: discovery alone cannot carry the governance burden. Identity teams need to decide whether their programme is monitoring agent behaviour or controlling the moment of execution, because those are not the same control objective.

Per-action intent validation: this is the control idea that should now sit alongside privileged access, not beneath it. If an approved agent can still make a destructive call inside an approved tool, then role permissioning has not actually solved the access problem. Align this work with the NIST AI Risk Management Framework where governance, measurement, and management have to meet at runtime.

The next stage for many programmes will be a tighter link between IAM, PAM, and AI engineering. That means treating agent execution paths as privileged workflows, then deciding which requests need just-in-time credentials, which need policy checks, and which need to be blocked entirely before they touch production.

For practitioners

• Map each AI security tool to its actual control layer Separate discovery, routing, workforce governance, orchestration, and runtime enforcement in your architecture map so you can see which layer only observes and which layer can stop execution.
• Audit whether standing credentials still exist for agent paths Review OAuth grants, API keys, connector secrets, and service credentials used by agents and determine whether they can still be reused outside the execution moment.
• Test for intent validation before production access Run scenarios where an approved agent attempts a destructive or unexpected action and verify whether the control layer can distinguish permitted tool use from unsafe request intent.
• Extend PAM governance to agent execution paths Bring AI agent workflows into the same privileged access oversight used for human and machine identities, including approval policy, audit trail, and credential lifecycle review.

Key takeaways

• AI agent governance is moving from visibility and routing to action-level enforcement, because the critical risk occurs at runtime.
• Discovery platforms and MCP gateways each solve part of the problem, but neither removes standing credentials or validates intent before execution.
• IAM and PAM teams should treat agent access as a privileged workflow that needs decision-path controls, not just tool permissions.

Key terms

• Runtime authority: Runtime authority is the permission an AI system has while it is actively deciding and acting, not just when it is approved. In governance terms, it is the point where access, tool use, and action scope become operational, which is why build-time review alone cannot prove safety.
• Standing Credential: A standing credential is any secret that remains usable until it is manually rotated or revoked. In NHI governance, it creates durable access that can be stolen, replayed, or propagated from trusted tooling unless runtime boundaries and expiry are built in.
• Callback Validation: Callback validation is the set of checks performed when the federated identity flow returns to the application. It confirms that the response came from the expected flow, belongs to the correct organisation, and can be exchanged safely for a local session. Weak validation creates a direct path from successful authentication to misissued access.
• MCP Gateway: The control layer that relays assistant intent to tools and data sources through the Model Context Protocol. In practice, it becomes a policy boundary, not just a transport layer. If it trusts model output too early, it can turn unverified reasoning into real-world execution or disclosure.

What's in the full article

Akeyless's full article covers the operational detail this post intentionally leaves for the source:

• How the Runtime Authority layer brokers requests and mints credentials just in time for specific agent actions
• How the vendor distinguishes discovery platforms, MCP gateways, workforce governance tools, and orchestration platforms in practice
• How runtime intent checks are applied before a production action executes
• How the architecture fits alongside existing privileged access and audit pipelines

👉 Akeyless's full post covers the architecture boundaries, complementary tool categories, and action-level governance detail.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.