Give each production agent a distinct identity, grant it only the permissions its task requires, and keep reusable service secrets outside the agent’s readable context. Prefer short-lived workload identity federation where the provider supports it; otherwise, have a trusted application or proxy add narrowly scoped credentials to approved requests. A secrets manager protects stored secrets, but it does not hide a secret from agent code that can read it.
That is the practical answer to a common engineering question: “How do you handle API keys when an AI agent needs access to multiple external services?” Treat every credential as authority the agent’s runtime might misuse, not as text the model needs to see.
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Start with the agent’s threat boundary
An agent can use what its runtime can reach: files, process environment, tools, and network destinations. A credential can therefore be exposed through source code, environment variables, prompts or context, tool outputs, or logs. It can also be misused after prompt injection or another compromise, even if nobody deliberately included it in a prompt.
OpenAI’s Sandbox security documentation warns that agent-generated code can access the files, credentials, and network available to its environment. The implication for production is straightforward: do not give agent code broader access than the task needs, and do not assume a secret is protected merely because it came from a vault.
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Give each workload its own identity
Assign a dedicated service identity to each agent workload or meaningful trust boundary. Avoid developer keys, shared human accounts, and credential reuse across agents. With a distinct identity, access can be attributed to a workload and limited or revoked without disrupting unrelated services.
NIST’s Back to the Future: Why Agentic AI Needs a Strong Identity Foundation recommends unique agent identifiers, credentials, and entitlements bound to the identity of the user or system operating the agent. It identifies OAuth 2.0 and SPIFFE as existing mechanisms relevant to enterprise agent identification and authorization. Dynamic tokens restricted by scope and audience are generally a stronger fit than static bearer credentials that work broadly.
Keep identity separate from authorization: identifying which workload is calling does not by itself establish which actions it may take. The downstream service or a trusted execution layer still needs to enforce the permissions appropriate to that workload.
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When can workload identity replace static keys?
Use workload identity federation when the agent runs in an environment that can prove its identity to a trusted provider and the service receiving the request supports federation. The workload presents a short-lived identity token from its identity provider—or, for supported API workloads, a verified certificate identity—and exchanges it for a short-lived service token. An administrator configures the trusted provider and a rule mapping that identity to a service account or workspace principal. The mapped principal’s permissions constrain what the resulting token can do.
OpenAI’s current federation documentation describes guidance for Kubernetes, AWS, Azure, Google Cloud, Oracle Cloud Infrastructure, GitHub Actions, and SPIFFE-compatible identity. This is documented support for particular OpenAI API and Codex paths, not a guarantee that every deployment or third-party service supports the same exchange. Administration and runtime configuration differ between those paths, so follow the instructions for the specific integration in use.
Federation reduces dependence on stored, long-lived OpenAI API keys for supported workloads. It does not make excessive permissions safe: a short-lived token with broad authority can still cause harm while valid.
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Choose how credentials reach external services
For an agent that needs several external services, decide separately for each integration whether it can use federation or must use a secret. Prefer a design in which the agent requests an operation and trusted application code performs the authenticated call; the model receives only the result it needs.
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|---|---|---|---|
| Workload identity federation | Not necessarily; the workload exchanges an identity proof for a short-lived service token. | The runtime and target service support a trusted identity exchange. | Provider support, trust configuration, and principal mapping vary. The mapped permissions still need tight limits. |
| Proxy or application-side broker | No, if the reusable credential stays in the trusted service and is never returned to agent code. | A third-party API requires a key, or a function tool can make the call on the agent’s behalf. | The broker must validate the requested operation and destination; an overly permissive broker can become a path to misuse. |
| Secret-manager retrieval | Yes, if the secret is retrieved into an agent-readable environment. | Protecting stored values and controlling which trusted application components can retrieve them. | A vault protects storage and retrieval access, not a secret after readable code receives it. |
| Static API key | Yes, wherever the key is placed in readable source, configuration, or runtime environment. | An integration has no suitable federation path and a trusted component must authenticate with a key. | The key can be reused until it expires or is revoked; scope, storage, and rotation need deliberate controls. |
OpenAI’s sandbox guidance specifically warns that injecting a stored secret into an agent-readable environment still exposes it to agent-generated code. For third-party calls, keep the key in a trusted proxy or application that adds it only to an approved request. For a function tool, retain the credential in the application handling the call and return the result rather than the key. A secret manager remains useful for protecting stored values, but it is not a boundary against code that can inspect a retrieved value.
For conventional application code that must use an API key, OpenAI recommends a key management service for production deployments. Keep keys out of browser or mobile client code and source repositories. Environment variables can prevent a value from being committed with source, but they do not stop code in the same process from reading the environment.
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Limit tools and actions independently of the model
Give the agent only the tools, data, and network destinations its task needs. Separate read-only access from write-capable access, use different identities or permission sets where practical, and restrict outbound connections to approved endpoints. Avoid standing privileges that let an agent take high-impact actions merely because it can call a tool.
OWASP’s AI Agent Security Cheat Sheet recommends separating decision-making from execution: a trusted policy or execution component should independently validate scope, privilege, and approval state rather than treating the model’s decision as authorization. For sensitive actions, bind an approval to the actor, tool, resource, normalized parameters, and a limited validity period. Use short-lived authorization artifacts and replay protection for irreversible operations. The approval check belongs in deterministic execution code, not in a prompt asking the model to be careful.
NIST likewise cautions that broad entitlements and standing privilege create risk because agents can move quickly through unexpected paths. A short-lived credential helps limit how long access remains usable; least privilege limits what it can do while usable. Apply both controls.
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Rotate credentials and prepare for revocation
Set a rotation process that matches the credential type and provider controls; the available guidance does not establish one universal rotation interval. For an expiring API key, OpenAI recommends creating a replacement before expiry, updating the application, verifying that the replacement works, and then revoking the old key. Administrators can set maximum key lifetimes at organization or project level.
- Prepare a replacement. Create a new key or credential with only the required permissions, without exposing it to agent-readable code.
- Update the trusted caller. Change the proxy or application that makes the external request, not the prompt or agent memory.
- Verify the new path. Confirm the application can authenticate and that expected operations succeed before removing the old credential.
- Revoke the old credential. Remove it once the new path is verified, or immediately if compromise is suspected.
- Review access evidence. Check provider usage and relevant application logs for activity that should not have occurred.
If a key may have leaked, revoke or rotate it promptly and update production applications. Usage monitoring and IP allowlisting from trusted infrastructure can add protection where supported. Do not rely on spend limits or alerts as containment: enforcement may not be immediate, and limits can also block legitimate requests.
For federation, review the external issuer, trust rules or service-account mappings, and the downstream principal’s permissions. The short lifetime of a token does not remove the need to correct an overly broad mapping or compromised trust configuration.
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Test the boundary before release and after material changes
OWASP recommends structured security testing before production and after material changes to prompts, tools, memory, retrieval, policies, or model providers. Include adversarial cases that check whether the agent or its tools can cross the intended authority boundary:
- Prompt override that requests an unauthorized action or attempts to reveal a secret.
- A tool request that exceeds the agent’s scope, including attempts to use write access when only reads are allowed.
- Privilege escalation through a tool, integration, or unexpectedly permissive identity mapping.
- Data exfiltration through tool responses, logs, or an unapproved network destination.
- Approval bypass, altered action parameters after approval, or replay of an authorization artifact.
- One compromised agent attempting to access another agent’s credentials, tools, or resources.
Use test credentials and synthetic data rather than live secrets or customer data in fixtures. Retain the tested agent version, provider, tool policy, retrieval configuration, abuse cases, expected outcomes, observed approvals and denials, and accepted residual risks with their compensating controls. This evidence makes it possible to determine what boundary was tested and what changed after a release.
Sources and scope
The provider-specific federation, key-lifetime, and key-safety points above reflect OpenAI’s official Workload identity federation, Sandbox security, and Help Center Best Practices for API Key Safety guidance, accessed October 7, 2026; the Help Center page stated it was updated September 29, 2026. NIST’s identity recommendations are from Back to the Future: Why Agentic AI Needs a Strong Identity Foundation. NIST’s separate New Concept Paper on Identity and Authority of Software Agents, released February 5, 2026, describes work exploring agent identity and authorization; it is not a final standard. OWASP’s recommendations are from its AI Agent Security Cheat Sheet; an exact publication or update date was not established.
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