Runtime machine IAM for AI agents closes the attribution gap

At a glance

What this is: Riptides says AI agents need runtime machine IAM because existing IAM cannot reliably control or attribute agent actions in production.

Why it matters: This matters because IAM, PAM, and NHI programmes must account for agents that act on behalf of people while changing tool use and credential needs at runtime.

By the numbers:

• Only 5.7% of organisations have full visibility into their service accounts.
• 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.

👉 Read Riptides' analysis of runtime machine IAM for AI agents

Context

AI agent identity is the problem of proving what an agent is, what it is allowed to touch, and who it is acting for at the moment it takes action. Existing IAM models were built around predictable services and human-driven sessions, so they struggle when the same agent can take different tool paths, request different credentials, and leave a weak audit trail from one run to the next.

The governance gap is not just access. It is also attribution and credential handling, because security teams need to answer who acted, on whose behalf, and with what authority after the fact. That is why runtime identity, just-in-time credentials, and zero-standing privileges have become the practical conversation for agentic AI programmes, not abstract architecture terms.

Key questions

Q: How should security teams govern AI agents that act on behalf of users?

A: Treat the agent as a distinct runtime identity and preserve the user context it borrows for the session. Governance must cover what the agent can reach, which credentials it can receive, and how each action is attributed. If you cannot explain who authorised the action and why, the control model is not ready.

Q: Why do AI agents create more attribution risk than normal workloads?

A: Agents can choose different tool paths, request different credentials, and trigger actions that span multiple systems in one session. That makes post-incident reconstruction far harder than with a stable service account. A usable audit trail must capture the agent, the user, the context, and the policy decision at execution time.

Q: When does JIT access reduce risk for AI agents?

A: JIT access helps when the agent needs to reach sensitive systems but does not need persistent standing privilege between actions. It reduces the window for token theft, prompt injection, and accidental overreach. It is most effective when the credential is issued for one call and never exposed to the agent process.

Q: What should IAM teams review first when agents start touching production systems?

A: Start with the identities the agents inherit, the credentials they can retain, and the systems they can call without approval. Those three areas reveal whether the environment is still running on service-account assumptions. If the answers are vague, policy enforcement and attribution will both fail under pressure.

How it works in practice

Why service-account IAM breaks for AI agents

Traditional service-account IAM assumes a stable workload identity that behaves the same way each time it runs. AI agents do not fit that model because tool selection, credential demand, and action order can vary by prompt, context, or prior output. That makes static entitlement models too blunt: they either over-grant and accept risk, or under-grant and block useful work. The result is a governance gap between what the agent can do at runtime and what the IAM stack can prove or enforce.

Practical implication: map agent identities to runtime controls, not to one fixed service account and a generic allow list.

Composite identity and session attribution

Composite identity binds the machine identity that launched the agent to the human or operator context that initiated the session. That matters because many agent actions are legitimate only in the context of a specific person, project, or request. If attribution only records a service account, investigations become log archaeology across traces, SIEM records, and application telemetry. A runtime model instead creates a single evidentiary trail that ties action, authority, and context together at the point of execution.

Practical implication: preserve session context with every agent action so investigators can reconstruct authorisation without manual correlation.

Just-in-time credentials and zero-standing privilege for agents

AI agents should not hold long-lived credentials in environment variables or memory if the same action can be brokered on demand. JIT access narrows exposure because the credential is issued only for the specific call that needs it, while zero-standing privilege prevents persistent access from accumulating across sessions. The security value is not cosmetic. It reduces the window for prompt injection, dependency compromise, and post-execution token theft, especially in environments where agents reach cloud APIs, GitHub, or Kubernetes.

Practical implication: move from reusable tokens to call-bound credential brokering for any agent that can reach sensitive systems.

NHI Mgmt Group analysis

AI agent identity is forcing IAM to confront runtime behaviour, not just entitlement design. Existing IAM programmes were built for identities that behave predictably between reviews. That assumption weakens when agents can choose different tools, request different credentials, and alter execution paths inside the same session. The implication is that identity governance for agents has to be judged on what it can prove at runtime, not on what it assigned at onboarding.

Composite identity is the named concept this category now needs. The article’s central insight is that an agent is often acting with two authorities at once: the machine identity that executes the action and the human context it borrows. That creates a governance problem that shared service accounts cannot resolve cleanly, because attribution becomes split across operational layers. Practitioners should treat this as a distinct identity pattern, not a cosmetic extension of workload identity.

Zero-standing privilege becomes operationally different when the subject is an agent. For humans and classic workloads, standing access is already risky. For agents that can request, use, and discard access within the same runtime window, persistent entitlements create a governance fiction: the access outlives the decision that justified it. The control model has to move from reviewable access to per-action authority.

Visibility and accountability are converging into one control requirement. Security teams do not only need to know what the agent touched. They need a defensible trail showing why the action was authorised and which session context applied. That elevates attribution from an audit feature to a core governance control for agentic AI programmes.

The market is moving toward host-level enforcement because application-layer controls are too late. If policies are checked after the agent has already made a tool call, the harmful decision has effectively escaped the control boundary. Host-level identity, policy, and credential enforcement reflects where the decision is actually made. Practitioners should expect agent governance stacks to converge around runtime enforcement rather than SDK-only instrumentation.

From our research:

• Only 5.7% of organisations have full visibility into their service accounts, according to Ultimate Guide to NHIs.
• 79% of organisations have experienced secrets leaks, and 77% of those incidents caused tangible damage.
• For a broader view of where identity programmes fail in practice, see 52 NHI Breaches Analysis.

What this signals

Composite identity will become a baseline requirement for agent governance. Programmes that still treat an agent as either a workload or a user proxy will miss the fact that both authorities matter at different points in the session. The operational task is to keep those authorities visible without forcing teams back into shared credentials or opaque delegation chains.

Only 30.9% of organisations store long-term credentials directly in code, according to our Ultimate Guide to NHIs study. That is still enough exposure to make runtime brokering, short-lived access, and host-level enforcement practical priorities for agentic workloads.

The next control gap will be in proof, not policy. If your team cannot show who initiated an agent action, what context it inherited, and whether the credential survived beyond the call, then your governance model is still relying on post hoc reconstruction instead of runtime control.

For practitioners

• Define agent identities separately from shared service accounts Assign each production agent a distinct runtime identity and bind it to the workload that actually executes. Shared credentials make attribution weak and privilege creep harder to see.
• Broker credentials at the call path Issue secrets only for the specific outbound request that needs them, and remove them from user space immediately after use. Long-lived tokens in shell environments and process memory are a standing exposure.
• Preserve session context in every agent action record Record the agent, the initiating user, the tool path, and the policy decision together so investigations do not depend on cross-correlating multiple logs later.
• Move enforcement below the application layer Check policy where the connection is made, not after the agent or framework has already selected a tool and sent data. Runtime enforcement reduces the gap between intent and control.

Key takeaways

• AI agents expose a governance gap that classic service-account IAM was never designed to close.
• Attribution, credential handling, and runtime enforcement become inseparable once an agent can act on behalf of a user.
• Security teams should move from standing access to call-bound authority before agent sprawl turns into audit failure.

Key terms

• Composite Identity: Composite identity is an identity pattern where a machine runtime and a human session both contribute to authorisation and attribution. It is useful for AI agents because the agent acts technically as a workload but often borrows human authority for the task, creating a single operational trail across both layers.
• Zero-standing Privilege: Zero-standing privilege means an identity has no persistent access beyond the immediate task it is performing. For agents, this is stronger than simple least privilege because the credential should exist only for the call or action being authorised, not as a durable entitlement the process can reuse later.
• Runtime Machine IAM: Runtime machine IAM is identity control enforced while the workload is executing, not just when it is provisioned. It combines attestation, policy, and credential brokering so the system can decide what an agent may do at the moment of action, which is essential when tool use changes session by session.

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 responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.

This post draws on content published by Riptides: Identity Riptides is now open to everyone. Read the original.