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The safest pattern is to give an AI agent a dedicated runtime identity, obtain short-lived credentials through the cloud provider or a trusted identity federation, and authorize that identity only for the resources and actions the agent needs. Never place service-account keys, client secrets, access tokens, or passwords in prompts, source code, chat history, or ordinary configuration.
First identify where the agent runs and whose authority it should use. A cloud-hosted workload may use managed identity; an external workload can often use workload identity federation; an agent acting for a signed-in person should use delegated user authorization; and an autonomous background agent should use its own app or workload identity. Authentication proves which identity presented a token. IAM and resource policies still decide what that identity can do.
What is the safest architecture for an AI agent accessing cloud services?
Put an identity-aware control plane between the model and every cloud API. The model can request an operation, but a trusted runtime component obtains a short-lived token, checks policy, calls the target service, and returns only the permitted result. The model never receives the underlying secret.
The identity should belong to the workload or to the user being represented, not to a shared administrator account. Give separate agents and environments separate identities so that logs, permissions, revocation, and incident response remain attributable.
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Authentication and authorization are different controls
Authentication establishes that a request came from a particular workload, user, or federated principal. Authorization then evaluates IAM roles, resource policies, scopes, conditions, and boundaries to decide whether that identity may perform the requested action. Google Cloud describes federated principals receiving access according to the IAM roles granted to them, so a successful token exchange alone does not grant useful access.
Keep these decisions separate in your design: first choose a trustworthy way to identify the caller, then grant the smallest set of permissions on the actual storage bucket, database, queue, API, or other resource.
How do I choose an identity pattern for my agent?
Answer two questions before creating any credentials: where does the agent execute, and whose authority should the action use?
Where does the agent run?
- Inside the same cloud as the target service: Prefer that cloud’s managed identity or managed workload identity when both the runtime and target service support it. Microsoft says managed identities let applications obtain Microsoft Entra tokens without developers managing credentials: Microsoft’s access-pattern guidance.
- Outside the target cloud: Prefer federation from the hosting environment’s trusted identity provider. Google Cloud Workload Identity Federation exchanges an external identity for short-lived Google credentials and can avoid creating a service-account key: Google’s workload identity documentation.
- Across several clouds: Define an explicit trust relationship for each hop. Verify the token issuer, audience, subject mapping, target resource, and authorization policy independently; an identity object from one provider is not automatically valid in another.
Whose authority should the agent use?
- A signed-in person’s authority: Use delegated access so downstream calls remain limited by that person’s permissions. Microsoft documents the OAuth on-behalf-of pattern for passing a user’s authorization to downstream APIs and warns against replacing it with a broader backend identity that silently bypasses user restrictions: Microsoft’s access-pattern guidance.
- Autonomous or scheduled work: Use a separate application or workload identity with app-only permissions limited to the job. Do not reuse a human administrator’s account or a general-purpose service account.
- Per-agent governance: Where supported, assign a unique identity to each agent instance or lifecycle. Microsoft Entra Agent ID guidance describes unique identities and blueprint-level controls; Google Cloud also describes identities associated with an agent lifecycle. Availability depends on the product and runtime, so verify support before designing around either feature: Microsoft Entra Agent ID best practices.
What are the differences between managed identity, federation, delegated access, and app-only access?
These patterns solve different combinations of hosting location, authority, credential lifetime, and operational ownership. None is a universal ranking; the target API and runtime must support the chosen method.
| Pattern | Best fit | Credential behavior | Authority and audit considerations |
|---|---|---|---|
| Managed identity | Workload running on a cloud platform that supports managed identity | Platform obtains and rotates tokens; no application-held secret | Identity belongs to the workload; grant resource-level roles and monitor token use |
| Workload identity federation | External workload authenticating through a trusted identity provider | Exchanges an external assertion for short-lived cloud credentials | Trust configuration, subject mapping, audience, and impersonation scope must be tightly constrained |
| Delegated user access | Agent acting for a signed-in person | Tokens represent the user and requested scopes; refresh handling depends on the provider | Preserves user-level authorization and attribution; do not replace it with an unrestricted backend identity |
| App-only workload identity | Autonomous, background, or scheduled operation | Short-lived tokens are preferred; a static key is a fallback with additional risk | Agent gets its own permissions and logs; separate it from human identities |
| Static service-account key | Only when a target or runtime cannot use safer mechanisms | Long-lived secret must be stored, rotated, monitored, and revoked | Possession can authenticate as the service account, so limit scope and exposure |
How do I connect an AI agent to cloud APIs step by step?
Use this sequence for a new integration or to review an existing one. Write down the decisions; they become the basis for access reviews and incident response.
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- Inventory the runtime and targets. Record the agent framework, hosting location, execution environment, target cloud, API endpoints, resources, data classifications, and whether a human is present for each action. Check that the runtime and every target service support the identity type you plan to use.
- Classify each operation as delegated or autonomous. If a user asks the agent to read or change that user’s data, preserve delegated user authorization through the request chain. For scheduled processing, monitoring, or queue consumption, create an app-only workload identity. Do not mix both models under one broad identity.
- Select the provider-supported exchange. Use managed identity when the workload and target support it. For an external Google Cloud workload, evaluate Workload Identity Federation before creating a service-account key; Google’s guidance explains the federation design and its constraints: Google’s Workload Identity Federation best practices.
- Create a dedicated identity. Assign a distinct identity to the agent, environment, or agent instance. Avoid sharing one credential across development, staging, production, or unrelated agents. A compromise then has a smaller blast radius and logs identify the responsible workload.
- Restrict the trust relationship. For federation, constrain the issuer, audience, subject or attribute mapping, allowed locations, and which service account or workload can be impersonated. Reject ambiguous mappings and avoid granting a federated principal permission to impersonate an entire project or organization.
- Grant least privilege on resources. Assign only the actions required for the agent’s documented job. Prefer resource-level roles over project-wide or account-wide administrator roles. Add IAM conditions, permission boundaries, network restrictions, or time limits where the provider supports them.
- Keep the exchange outside the model context. The token request, token cache, secret storage, and API call should run in trusted application code or platform infrastructure. Pass the model a narrowly shaped tool result, not an access token, client secret, signed request, or credential-bearing error message. The identity mechanism does not automatically isolate prompts, so enforce this boundary in your own runtime.
- Test both allowed and denied paths. Verify that the agent can perform each required operation and that it receives an explicit authorization failure for unrelated resources, destructive actions, or another user’s data. Test expired tokens, revoked access, malformed audience values, and a compromised tool request.
- Log and review usage. Record the agent identity, user identity when delegated, target resource, action, policy decision, token issuer, request correlation ID, and outcome. Review permissions and token use regularly; define an owner and a documented disable or revoke procedure before production launch.
- Document recovery. Specify how to disable the agent, remove role bindings, invalidate refresh tokens if used, rotate any remaining secrets, preserve audit records, and restore service with a clean identity after an incident.
How should delegated user access work?
When an agent acts for a person, the downstream service should see that person’s delegated authorization rather than an all-powerful backend account. The front end authenticates the user, the agent service validates the incoming token, and the service obtains a downstream token for the approved audience and scopes.
Use on-behalf-of flow for downstream APIs
Microsoft’s on-behalf-of guidance describes exchanging a user token for a token intended for a downstream API. Validate issuer, audience, signature, expiry, tenant or organization, and required scopes before the exchange. Pass only the resulting downstream token to the API client; never expose either token to the model.
Prevent confused-deputy behavior
Bind every tool call to the authenticated user and intended tenant. Do not accept a user ID, project ID, or resource name supplied only by model output as proof of authority. Resolve those values against server-side authorization checks, and require explicit confirmation for high-impact actions such as deleting data or changing access policy.
How do managed identities and federation avoid long-lived secrets?
A managed identity is attached to a supported workload, allowing the platform to issue tokens without an application storing a client secret. Federation instead trusts an external identity provider and exchanges a short-lived assertion for cloud credentials. Both reduce the number of durable secrets that can be copied from source code, build logs, images, or configuration.
Support is conditional. Confirm that the compute service can present the managed identity, that the target API accepts the resulting token, and that the federation provider supports the required claims and token audience. A pattern that works for one compute product or API may not work for another.
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Google Cloud federation checks
Google recommends careful attribute mapping and narrowly scoped impersonation in Workload Identity Federation. Ensure that only the intended external subjects can impersonate the target service account, and avoid wildcard subjects or project-wide grants. The federation pool, provider, service account, and target resource should all be represented in your threat model.
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Microsoft Azure and Entra
Managed identity is appropriate when the workload runs on Azure, the runtime exposes that identity, and the target service supports Microsoft Entra authentication. Microsoft distinguishes delegated user access from app-only access, so choose scopes and role assignments according to the authority model rather than simply attaching a broad managed identity. Entra Agent ID guidance adds governance concepts for unique agent identities and blueprint controls, but availability is product-specific: Microsoft Entra Agent ID best practices.
Google Cloud
Google Cloud supports service accounts as workload identities and Workload Identity Federation for external workloads. Managed workload identity or agent-identity features may apply in supported environments. Service-account keys remain technically possible but should be treated as a risk-bearing fallback; Google warns that possession of a key enables authentication as that service account: Google’s service-account key guidance.
AWS
The AWS Well-Architected Agentic AI Lens states that agent-to-agent and agent-to-service communication should authenticate through verifiable mechanisms, including certificate-based mutual TLS, signed OAuth tokens, or platform-managed workload identity: AWS Agent identity and permission management. It also emphasizes separate agent and human permissions, least privilege, short-lived credentials, and auditable identity. The guidance is a security framework rather than a complete setup recipe for every AWS compute service or agent framework, so map the principles to the specific runtime you deploy.
How do I stop credentials leaking through prompts, tools, and logs?
Design the agent as an untrusted consumer of capabilities. The model should see tool names, parameter schemas, and sanitized results, while a trusted service performs token acquisition and API calls.
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- Keep secrets and token caches in the platform’s secret or identity subsystem, not in system prompts, conversation history, environment variables copied into model context, notebooks, or source repositories.
- Use allowlisted tools with typed parameters. Reject arbitrary URLs, headers, authorization fields, and user-controlled token audiences.
- Redact access tokens, refresh tokens, cookies, signed URLs, private keys, and authorization headers from traces, error messages, analytics, and prompt-debugging dashboards.
- Set short token lifetimes and renew them in the trusted runtime. Do not ask the model to refresh a token or decide whether a token is still valid.
- Separate data returned to the user from control metadata. A tool result should not include raw HTTP headers or provider claims unless the application explicitly needs them.
- Treat retrieved documents and tool output as untrusted input. Prompt injection can attempt to persuade an agent to disclose credentials or call an unrelated resource; server-side authorization must remain decisive.
How do I apply least privilege to an agent?
Start with the agent’s smallest useful job, then map each tool operation to a specific resource and permission. A report-generating agent may need read access to one dataset and write access to one output location, but not permission to enumerate every project or change IAM policy.
Use separate identities for separate duties
Split ingestion, transformation, notification, and administration into different identities when their permissions differ. Development and production should not share role bindings. If one agent needs an exceptional operation, use a separate, approval-gated tool or temporary elevation rather than permanently broadening its normal identity.
Add boundaries and conditions
Where supported, constrain access by resource name, request attributes, network or workload location, time window, and allowable operation. Permission boundaries and deny policies can provide a backstop, but they do not replace reviewing the positive grants that an identity already has.
How should I monitor, audit, and revoke agent access?
Make every action attributable to both the agent and, when applicable, the human who initiated it. Microsoft recommends monitoring token usage and auditing permissions; AWS guidance likewise emphasizes ongoing access review and drift validation.
- Log identity context: agent or workload ID, user ID for delegated calls, tenant, token issuer, audience, and authentication method.
- Log authorization context: target resource, operation, role or policy decision, condition result, and whether approval was required.
- Detect drift: alert on new role bindings, changed federation mappings, unusual token audiences, access from unexpected runtimes, and calls outside the agent’s normal resource set.
- Review regularly: remove unused permissions, rotate or retire identities when an agent version is replaced, and verify that disabled agents cannot obtain new tokens.
- Prepare an emergency path: disable the workload, remove role assignments, revoke user grants or refresh tokens where applicable, block the federation subject, and preserve logs for investigation.
What should I do if a static key is unavoidable?
Use a static service-account key only when the runtime or target genuinely cannot support managed identity, federation, or another short-lived mechanism. Google documents the leakage and privilege-escalation risks of service-account keys, so treat the key as an incident-prone secret rather than a normal configuration value: Google’s service-account key guidance.
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- Create a service account dedicated to one agent and environment.
- Grant only the resource-level permissions required for that workload.
- Store the key in an approved secrets manager with access logging and strict operator permissions; never commit it to a repository or bake it into an image.
- Inject it only into the trusted connector process, not into prompts or model-visible variables.
- Set an owner, expiration or rotation schedule, and alerting for use from unexpected locations.
- Revoke the key immediately if it appears in source, logs, tickets, telemetry, or a conversation transcript. Then investigate all activity performed by the service account.
What commonly goes wrong, and how can I fix it?
| Symptom | Likely cause | Fix |
|---|---|---|
| Token exchange succeeds but API calls return permission denied | Authentication was configured, but the identity lacks a role on the target resource or the token has the wrong audience | Check the target audience, principal mapping, resource-level role, conditions, and service-specific authorization policy |
| An agent can read another user’s data | A backend app-only identity replaced delegated user authorization | Use delegated or on-behalf-of access for user actions and enforce user and tenant checks server-side |
| Federation accepts unintended callers | Wildcard subject or overly broad attribute mapping | Restrict issuer, subject attributes, audience, and impersonation permissions; test a near-match identity |
| Credentials appear in traces or model messages | Raw headers, environment variables, or exceptions are being passed through the agent framework | Redact at the connector boundary, return structured errors, and remove secrets from stored prompts and logs |
| Revoking a role does not stop activity immediately | Cached tokens, refresh tokens, or a second identity remain active | Disable the workload, revoke applicable tokens, remove every binding, and inspect audit logs for alternate credentials |
| The design works in one environment but not another | Managed identity or target-service support differs by runtime, region, edition, or API | Verify support for each runtime-target pair and use federation or a different supported identity method where necessary |
What do practical agent architectures look like?
A user-facing data assistant
A user signs in, asks the assistant to summarize files, and receives only files that the user can read. The front end passes a validated user token to the agent service; the service performs an on-behalf-of exchange for the storage API, applies tenant and resource checks, and returns document content without exposing tokens to the model.
A nightly data-processing agent
A scheduled workload runs under its own production identity. It can read one input bucket, write to one output location, and publish to one queue. No human administrator credentials are present, and a separate staging identity prevents test jobs from reaching production data.
An external multi-cloud agent
An agent hosted outside the target cloud authenticates to a trusted external identity provider. The target cloud maps a narrowly defined subject to a workload identity through federation, then issues short-lived credentials for one API audience. Each cloud has its own explicit trust and policy checks; the agent does not assume that a token valid in one cloud is accepted by another.
What is the final design checklist?
- Runtime, target APIs, resources, and user-versus-autonomous authority are documented.
- Managed identity or federation was evaluated before any static key was created.
- Each agent and environment has a distinct, attributable identity.
- Authentication, authorization, and token audience are tested independently.
- Roles are resource-scoped and constrained with conditions or boundaries where available.
- Token exchange and secret storage occur outside model prompts and conversations.
- Tools reject arbitrary destinations and authorization parameters.
- Audit logs capture agent, user, resource, action, and policy outcome.
- Permission review, drift detection, disablement, revocation, and recovery procedures have named owners.
- Any unavoidable key has limited scope, protected storage, rotation, monitoring, and a documented emergency revocation process.
Frequently Asked Questions
Can an AI agent use an API key stored in an environment variable?
It can, but an environment variable is not automatically safe if the agent framework can print, retrieve, or insert that value into context. Keep the variable confined to a trusted connector process, prefer short-lived identity tokens, and ensure diagnostic output redacts it.
Should every agent have its own cloud account?
Usually the important separation is a distinct workload identity and narrowly scoped permissions, not a separate top-level cloud account. Separate accounts or projects can add isolation for high-risk environments, but they also increase operational overhead and do not replace per-agent authorization.
How long should an agent access token last?
Use the shortest lifetime that allows the specific job to complete, with automatic renewal performed by trusted runtime code. The appropriate duration depends on provider limits, task duration, retry behavior, and whether revocation controls can quickly stop a compromised workload.
What should I do when a tool needs a signed URL?
Generate the URL server-side for one resource, operation, and short expiration, then return only the URL if the user genuinely needs it. Do not give the model signing credentials, and prevent the agent from changing the resource path or extending the expiration through tool parameters.
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How can I prove which person caused an autonomous agent action?
Record the initiating user’s authenticated identifier and consent or approval event separately from the agent’s workload identity. Correlate both IDs in an immutable audit system so investigators can distinguish who requested an action from which software identity executed it.
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