Runtime access control for AI agents in Claude Enterprise

At a glance

What this is: Akeyless argues that AI agents accessing production systems through MCP need runtime, intent-aware control because authentication alone cannot constrain what an agent does mid-session.

Why it matters: This matters because IAM, PAM, and NHI teams now have to govern not just access to systems, but the actions an autonomous or semi-autonomous agent can take after access is granted.

By the numbers:

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

👉 Read Akeyless's analysis of runtime access control for AI agents in Claude Enterprise

Context

AI agent identity is no longer a theoretical governance issue. In Claude Chat, Cowork, and Claude Code deployments, the practical pattern is to grant production access through MCP configuration and then assume the session will stay inside its intended bounds. The problem is that identity controls still answer who can connect, while the harder question is what the agent can do once connected.

For IAM and NHI programmes, that gap is structural. Long-lived credentials in configuration files create obvious secret exposure, but even short-lived or valid credentials do not solve intent mismatch, prompt injection, or destructive action selection. When the actor can choose actions at runtime, access governance has to shift from entry control to session control.

This article is typical of the current market moment: agent access is being wired into enterprise systems faster than governance can keep pace. That makes runtime policy, auditability, and least-privilege session brokering the real control plane for AI agent identity.

Key questions

Q: How should security teams govern AI agents that use service accounts and MCP tools?

A: Start with ownership, then add runtime attribution and containment. Security teams should know which human deployed the agent, which identity the agent uses, what tools it can invoke, and when to revoke access. If the agent can chain tool calls or spawn sub-agents, governance must cover those paths as well, not just the initial login.

Q: Why do valid credentials still fail to protect against AI agent mistakes?

A: Because a valid credential only proves the system recognised the identity. It does not prove the requested action matched user intent, safe context, or business purpose. An agent can still be prompted, misled, or confused into actions that are technically authorised but operationally harmful, which is why action-level policy matters.

Q: What do teams get wrong about AI agent access reviews?

A: Teams often assume an access review can certify an agent the same way they certify a human or a service account. That fails when the agent’s behaviour changes session by session, because the review describes a static snapshot while the risk is dynamic execution. Review evidence should include tool use, action logs, and revocation tests.

Q: Who is accountable when an AI agent takes an unsafe action?

A: Accountability should sit with the business owner of the agent, the team that provisioned the access, and the control owners responsible for monitoring and revocation. If no one can answer who approved the identity, the scope, and the oversight model, the governance framework is not complete enough for production.

How it works in practice

Why long-lived credentials in MCP configs are the first failure mode

When an AI agent is connected through MCP, the fastest implementation path is often to place a password, token, or API key directly into a configuration file such as mcp-servers.json. That turns a secret into part of the agent operating environment, where it can be exposed through logs, processes, file reads, or compromised tooling. This is classic NHI exposure: the credential becomes portable, persistent, and recoverable outside the intended control boundary. The issue is not just theft. A credential stored near the model also broadens the blast radius if the agent or surrounding toolchain is compromised.

Practical implication: remove embedded secrets from MCP configs and treat any agent-accessible secret as already widened in scope.

Why valid credentials still do not make agent actions safe

A valid credential proves the system accepted the identity, not that the resulting action matched human intent. An AI agent can receive a legitimate request, interpret it poorly, or follow a prompt injection into a destructive command such as DROP TABLE. This is where standard authentication and RBAC stop being sufficient, because those controls decide access up front but do not evaluate the semantic appropriateness of each action. For agentic systems, the key distinction is between authenticated access and acceptable execution. Runtime authorisation has to sit between those two moments.

Practical implication: add policy checks at the moment of action, not only at session start.

How just-in-time brokering changes the control boundary

Just-in-time brokering narrows privilege to a single approved action, then expires the credential immediately after use. Architecturally, that means the agent never receives standing access it can reuse later, and the credential never lives inside the model context or configuration files. In governance terms, the control boundary moves from provisioning-time trust to action-time enforcement. That is a better fit for agentic workflows because the agent may be active across multiple tools, but only a subset of those actions should ever be authorised in a given session.

Practical implication: use brokered, per-action access for high-risk systems rather than granting persistent agent credentials.

NHI Mgmt Group analysis

Standing credentials are the wrong mental model for agent identity. AI agents connected through MCP behave less like ordinary service accounts and more like runtime decision systems that can choose actions after access is granted. That means persistent credentials create both exposure and governance drift, because the session can continue long after the original trust decision has gone stale. The practitioner conclusion is that agent identity must be treated as an action-bearing session, not a static login.

Intent-aware policy is now the missing control plane for AI agent governance. Traditional IAM answers whether a credential is valid, but it does not answer whether a requested database query, file operation, or cloud action matches the user’s purpose. That gap becomes visible when an agent follows an ambiguous instruction or a prompt injection into destructive behaviour. The implication is that policy must evaluate request context at runtime, not just identity at authentication.

Ephemeral credential trust debt is accumulating faster than teams can see it. The more agents are wired into production with one-off connectors, the more organisations inherit hidden access assumptions they cannot recertify cleanly later. This is a named concept worth tracking because short-lived implementation choices can still leave long-lived governance debt if the surrounding controls are not designed for action-level expiry. Practitioners should treat every agent integration as future audit exposure unless the credential lifecycle is already invisible to the model.

AI agent governance is converging with PAM, but not as a simple extension of human privilege controls. PAM has always managed high-risk access, yet AI agents complicate the model because the actor can move from request to execution without human pacing in the middle. That makes session-level traceability, bounded privilege, and immediate revocation more important than the familiar vocabulary of user entitlement reviews. The practical conclusion is that PAM teams now own a material part of AI governance whether they planned to or not.

What this article signals about the market is a shift from identity proof to execution control. The industry is moving toward tools that broker access at the moment of action, because static trust decisions are too coarse for agents that may interact with databases, cloud APIs, and SaaS systems in the same session. That does not replace IAM or secrets management. It re-ranks them behind runtime enforcement as the control most likely to decide whether agentic deployments remain governable.

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.
• 48% of organisations say they cannot fully track and audit the data their AI agents access, which leaves a compliance and investigation blind spot that is already operational.
• For a broader view of agentic risk patterns, OWASP Agentic Applications Top 10 helps map runtime threats to governance controls.

What this signals

Ephemeral credential trust debt: once AI agents are allowed to reach production through temporary-looking but model-visible credentials, teams inherit a governance debt that outlives the session. The practical issue is not only secret exposure, but the inability to prove later which action was authorised and which was merely possible. That is why runtime brokering is becoming a core control for AI agent identity programmes.

As agent use spreads across Claude Chat, Cowork, and Claude Code, the governance question shifts from access enablement to execution containment. Security teams should expect more pressure to align agent controls with PAM, audit, and least-privilege review processes, especially where production databases and cloud APIs are in scope. The control objective is to keep the model out of the credential path and the credential out of the model path.

With 80% of organisations already reporting out-of-scope agent behaviour, according to AI Agents: The New Attack Surface, the next programme milestone is not broader access. It is the ability to prove, in one audit trail, why each agent action was allowed and what was blocked.

For practitioners

• Remove agent-visible long-lived secrets Audit MCP configuration files, environment variables, and adjacent toolchains for passwords, tokens, API keys, and certificates that an AI agent can read or inherit. Replace them with brokered access paths that keep credential values outside the model context.
• Require policy checks at action time Insert runtime authorisation before database writes, destructive shell commands, cloud API calls, and SaaS updates so each request is evaluated against purpose, scope, and destination. Treat authentication as necessary but not sufficient.
• Constrain agents to just-in-time access Use per-action or per-task credentials with immediate expiry after execution completes, especially for production databases, SSH, Kubernetes, and cloud services. Do not let agents reuse standing access across multiple prompts or tasks.
• Log prompt-to-action lineage Capture the originating prompt, the policy decision, the brokered credential event, and the target system response in one audit trail so investigations can reconstruct why the agent was allowed to act.
• Reclassify AI agent access as PAM scope Bring agent runtime access into PAM governance so high-risk actions, approval boundaries, and emergency revocation are managed consistently across human and non-human privileged activity.

Key takeaways

• AI agent governance fails when teams treat runtime systems like static human logins rather than action-bearing identities.
• Industry data shows that out-of-scope agent behaviour is already common, which makes action-level policy and auditability an immediate control need.
• The most effective safeguard is to remove standing credentials and broker access only at the moment an agent is authorised to act.

Key terms

• Agentic Runtime Authority: A runtime control model that evaluates an AI agent's request before it reaches a target system and can stop the action if it exceeds approved intent. It combines policy enforcement, session boundaries, and auditability so autonomy is constrained at the moment of execution.
• 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.
• 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.
• Model Context Protocol: Model Context Protocol is an open protocol that lets AI agents connect to tools and data sources. It expands what an agent can reach, so governance has to cover not only the model and its prompts, but also every system that can receive or return agent-driven data.

What's in the full announcement

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

• Step-by-step walkthrough of the Gateway-mediated access flow across Claude Chat, Cowork, and Claude Code.
• Specific examples of how just-in-time credential injection works for databases, Kubernetes, SSH, and cloud APIs.
• Implementation details for keeping credentials outside the model context while preserving forensic traceability.
• How the Compliance API integrates with runtime policy enforcement in practice.

👉 The full Akeyless post covers the Gateway workflow, policy enforcement points, and audit trail mechanics.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle 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.