Runtime authorization for AI agents: the IAM gap now exposed

At a glance

What this is: This is an analysis of why AI agents need runtime authorization, and the key finding is that authentication and static access controls do not stop agents from taking unintended actions after they connect.

Why it matters: It matters because IAM, PAM, and NHI programmes now have to govern not just who or what connects, but what an agent is allowed to do in the session where decisions and tool use happen.

By the numbers:

• 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.

👉 Read Akeyless's analysis of why AI agents need runtime authorization

Context

AI agent runtime authorization is the control problem that appears when an autonomous system can decide what to do, which tool to call, and when to act after it has already authenticated. The core issue is not just credential exposure, but the mismatch between connection-time access decisions and runtime behaviour in Claude, Chat, Cowork, and Claude Code-style workflows.

That gap lands directly in IAM, PAM, and NHI governance. Long-lived credentials in MCP files, over-broad SaaS tokens, and production database access all create a path where the agent can outgrow the scope that was approved for it, even when initial authentication succeeded cleanly.

Key questions

Q: How should security teams limit the risk from AI agents that have access to production systems?

A: Security teams should scope every agent to the smallest set of actions and resources needed for its task, then remove standing privilege wherever possible. Use short-lived credentials, explicit approval for sensitive actions, and continuous review of what each identity can reach. The goal is to make compromise hard to turn into lateral movement or data exfiltration.

Q: Why do long-lived credentials create a bigger risk for AI agents than for traditional automation?

A: AI agents can choose tools and sequence actions dynamically, so long-lived credentials become durable authority across many unpredictable requests. That makes it harder to prove least privilege, track accountability, or limit blast radius. Traditional automation is usually fixed and bounded, while an agent can reuse the same secret in ways the original design did not anticipate.

Q: What breaks when AI agent access is reviewed only after the fact?

A: After-the-fact review leaves a gap between action and containment. If an agent can already reach a dataset, API, or SaaS system, the damage may be done before a human sees the alert. Runtime checks reduce that gap by stopping unauthorized actions before they execute.

Q: How do you know if AI agent authorization is actually working?

A: Authorization is working when each agent action can be tied to a current identity, a current policy, and a specific data or resource scope. If access reviews cannot explain who approved the entitlement, or logs cannot reconstruct the decision, the control is not operationally effective.

How it works in practice

Why authentication is not runtime authorization for AI agents

Authentication answers a narrow question: can this identity connect? Runtime authorization answers the harder one: should this identity take this specific action right now, given the current task, context, and data sensitivity? For AI agents, those are not the same decision. An agent can authenticate with a valid OIDC flow, receive access, and still behave unpredictably once prompts, tool output, and chain-of-thought style planning reshape its next move. That is why credential possession and behavioural safety cannot be treated as equivalent. Practical implication: separate connection approval from action approval in every agentic workflow.

Practical implication: treat authentication as the start of policy enforcement, not the end of it.

How MCP credential handling creates NHI exposure

Model Context Protocol connects agents to tools and data sources, which makes it an identity boundary as much as an integration layer. When long-lived secrets are pasted into MCP configuration files, the agent environment becomes a repository for credentials that were never meant to be exposed to the model session. That raises classic NHI risks such as secret leakage, over-scoped access, and poor revocation hygiene, but now inside an agent runtime that may act repeatedly and at machine speed. Practical implication: broker access so the model never receives reusable secrets.

Practical implication: remove reusable credentials from agent-visible configuration wherever possible.

Why just-in-time least privilege needs intent-aware policy

Just-in-time access is necessary but not sufficient when the requester is an AI agent. A short-lived token still grants too much power if the policy only checks identity at issuance time and ignores the action being attempted. Intent-aware policy means the decision point moves to the moment of execution, where the request, target resource, and sensitivity of the operation can all be evaluated together. That is the difference between allowing an authenticated session and approving a specific database query, cloud action, or SaaS call. Practical implication: bind access to the action context, not only to the agent identity.

Practical implication: evaluate each privileged action at runtime against policy and task context.

NHI Mgmt Group analysis

Runtime authorization is the missing control plane for agentic identity. Authentication and access controls were designed to decide who can connect, not what an AI agent can safely do after it connects. That design assumption breaks once the actor can choose actions at runtime, because connection approval no longer limits behavioural risk. The implication is that identity governance for agents must shift from possession-based access to action-based authorization.

Standing credential exposure becomes a runtime behaviour problem, not just a secrets problem. Long-lived credentials in MCP files create the old NHI weakness, but autonomous execution turns it into a live decision-risk issue because the model can use those credentials in ways the operator did not intend. Ephemeral credential trust debt: the organisation still carries the operational risk of a reusable secret even when the session looks temporary. Practitioners should read this as a control-gap signal across secrets, IAM, and PAM.

Access review assumptions collapse when the actor decides and acts within the same session. Access review was designed for conditions where privilege persists long enough to be observed, recertified, and removed. That assumption fails when an AI agent can acquire access, act, and complete a tool sequence before any human review cycle can intervene. The implication is that lifecycle governance must be rethought for machine-paced execution, not merely scheduled more often.

Intent-aware policy is now a governance requirement, not an optimisation. Akeyless is pointing at a problem the broader market is also recognising: an authenticated agent can still execute the wrong action because user intent and LLM behaviour diverge. This is where OWASP Agentic Applications Top 10 and NIST AI Risk Management Framework style thinking become relevant, because the control question is about action validation, not just access entitlement. Practitioners should treat agent actions as high-risk identity events.

Forensic traceability has to cover the action, not only the login. If the record stops at successful authentication, the organisation cannot prove whether the agent acted within policy, exceeded scope, or caused damage through chained tool calls. That is a governance failure across NHI, PAM, and emerging agent controls. The practical conclusion is that auditability must be tied to each privileged decision point in the agent workflow.

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.
• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to AI Agents: The New Attack Surface report.
• For a deeper control lens, OWASP Agentic Applications Top 10 and NIST AI Risk Management Framework both frame the governance problem as action-level risk, not just login control.

What this signals

Runtime authorization will become the dividing line between governable agents and noisy automation. As more teams place AI agents into production workflows, the programme question changes from whether the agent can authenticate to whether each action can be bounded, logged, and denied in context. The practical signal is simple: if you cannot show action-level decisions, you do not yet have agent governance.

Ephemeral access patterns will force IAM and PAM teams to redesign evidence models. The next control gap is not just secret sprawl, but proof that a machine identity acted within policy at the exact moment of execution. Teams should expect stronger scrutiny of agent logs, action traces, and the lifecycle of temporary credentials, especially in environments that also use OWASP Agentic AI Top 10.

Ephemeral credential trust debt: organisations that keep reusing long-lived credentials for agents are accumulating governance debt that will surface in audit, incident response, or both. With 80% of organisations already reporting out-of-scope agent behaviour, per AI Agents: The New Attack Surface report, the case for runtime policy is no longer theoretical.

For practitioners

• Remove reusable secrets from agent-visible configuration Shift MCP and similar integrations so the model never receives long-lived credentials directly. Broker access through a gateway or token service that can issue short-lived credentials only when the action is approved.
• Bind authorization to the requested action Evaluate the resource, operation, and data sensitivity at runtime instead of granting broad session-level access. If the request changes from the approved task, the policy decision should change with it.
• Separate authentication from action approval Keep OIDC or other login methods for identity proofing, but add a distinct runtime policy layer for database queries, cloud operations, and SaaS actions that the agent attempts after login.
• Instrument full forensic traces for agent actions Record the prompt, target resource, policy decision, and resulting tool call so investigators can reconstruct what the agent did, not just that it authenticated successfully.
• Review PAM scope for machine-paced execution Identify any privileged workflow that still assumes a human operator can approve, pause, or intervene before the session completes, then redesign those paths for agent-speed actions.

Key takeaways

• AI agent governance fails when teams stop at authentication and never decide what the agent may do after login.
• The control gap is now visible at scale, with most organisations already seeing agents act beyond intended scope.
• Runtime authorization, short-lived credentials, and action-level audit trails are the controls that change agent risk from opaque to governable.

Key terms

• Runtime Authorisation: Runtime authorisation is the practice of deciding access while a task is in progress, rather than only at provisioning time. It matters for NHIs because credentials and entitlements can change risk mid-session, especially when automation or AI agents interact with sensitive systems.
• Intent-Aware Policy: A policy model that evaluates what an actor is trying to accomplish, not just which technical action it requested. For agents, this matters because the same tool call can be legitimate or risky depending on prompt, context, and the sequence of actions that follows.
• MCP Configuration Secret Exposure: MCP configuration secret exposure occurs when reusable credentials are placed where an AI agent runtime can read or reuse them. This turns a connecting protocol into a secret-handling risk, because the model may inherit access that was meant to stay outside the session boundary.
• Interaction-Level Audit Trail: A record that captures the full AI session rather than only network traffic or file events. It ties the prompt, model response, identity, and policy response together so auditors can reconstruct what happened and why the control acted the way it did.

What's in the full announcement

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

• Step-by-step setup for deploying the gateway and connecting Claude through MCP.
• Configuration flow for OIDC authentication with Microsoft Entra ID and a MySQL target resource.
• Dynamic secret creation and guardrail policy setup for runtime-enforced access decisions.
• Walkthrough material showing how policy is applied in a live Claude session.

👉 Akeyless's full post covers the setup guide, live session walkthrough, and policy enforcement flow.

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 identity security programme, it is worth exploring.