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Use an identity attached to the agent’s runtime, or federate a short-lived identity from its hosting environment, and grant that identity only the permissions the task requires. If the agent is acting for a signed-in person, preserve that person’s delegated authorization instead of substituting a powerful backend account. Do not put cloud keys in prompts, source code, chat history, or ordinary configuration. Authentication proves which workload or user is calling; cloud IAM and resource policies still decide what that identity may do.
The right design starts with two questions: Where does the agent run? and Whose authority should it use? The answers determine whether you need a managed identity, workload federation, delegated user tokens, or a dedicated app identity.
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What does it mean to connect an AI agent without exposing credentials?
A secure connection separates the agent’s reasoning from the credential exchange. The model can decide that it needs to read a storage object or call an API, but a trusted runtime component obtains a token and sends the request under a controlled identity. The token, private key, or client secret is never pasted into the prompt or made part of the agent’s conversation.
There are two distinct security decisions:
- Authentication: proving which user, workload, or agent instance is making the call.
- Authorization: deciding which resources and operations that authenticated identity is allowed to use.
Google’s workload-identity guidance makes the distinction explicit: a federated principal receives access according to the IAM roles granted to it. A successful token exchange therefore does not grant broad cloud access by itself. Review the target service’s IAM and resource policies as carefully as the identity setup.
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For a concise capability goal, the AWS Well-Architected Agentic AI Lens says: “Every agent-to-agent and agent-to-service communication authenticates through verifiable mechanisms, whether that is certificate-based mutual TLS, signed OAuth tokens, or platform-managed workload identity.” Read the full guidance at AWS Agent identity and permission management.
How do I choose the right identity pattern?
Choose the pattern from the agent’s hosting location and the authority it should exercise. A cloud-hosted autonomous worker normally uses a workload identity; an assistant acting for a person normally uses delegated user access.
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|---|---|---|---|
| Agent runs on the same cloud as the target service | Managed identity or provider-supported workload identity | The runtime and target service both support that identity mechanism | Support differs by compute product and target API; verify both |
| Agent runs outside the target cloud | Workload identity federation | The hosting environment has a trusted identity provider and the cloud can validate its assertions | Use precise subject and audience mappings; avoid broad impersonation permissions |
| Agent acts for a signed-in person | Delegated user authorization, such as an on-behalf-of flow | The downstream API accepts the user’s delegated token and scopes | Do not replace the user with a more powerful backend identity |
| Agent performs autonomous background work | Dedicated app or workload identity | The identity has only the roles required for that job | Keep it separate from human accounts and unrelated agents for attribution |
Managed identity is not a universal cloud feature. Microsoft describes managed identities for applications running where Microsoft Entra authentication is supported, while Google documents attached service accounts and managed workload identities for supported environments. Federation is likewise dependent on the external provider, token audience, runtime, and target resource. Confirm all four before committing to an implementation.
What is the difference between delegated user access and app-only access?
Delegated access carries a person’s authority through the request chain. App-only access represents the application or workload itself and can operate without a user session. Use delegated access when the agent is an assistant for a person; use app-only access for scheduled or autonomous work that has its own business responsibility.
| Characteristic | Delegated user access | App-only or workload access |
|---|---|---|
| Authority source | The signed-in user plus the application’s approved scopes | The agent’s service or workload identity and its IAM roles |
| Best fit | Reading or changing data the user is allowed to access | Background processing, scheduled jobs, and service-to-service calls |
| Audit attribution | Can show both the user and the calling application | Shows the dedicated workload or agent identity |
| Primary risk | Dropping the user context and accidentally using broader backend rights | Granting the autonomous identity more access than its job requires |
Microsoft’s AI-agent access-pattern guidance describes the on-behalf-of pattern for downstream APIs and warns against using a backend identity to bypass a user’s permissions. Preserve the user’s scopes, consent, and authorization failures rather than silently retrying with an app-only token.
How do managed identities and workload federation avoid long-lived secrets?
A managed identity is attached to a supported cloud workload. The platform proves which workload is running and issues a token without requiring developers to create or distribute a password-like secret. Microsoft summarizes the benefit this way: “Managed identities enable applications to get Microsoft Entra tokens without developers managing credentials.” See Microsoft’s access-pattern documentation.
Workload identity federation applies the same principle across a trust boundary. An external runtime authenticates to its own identity provider, presents a signed assertion, and exchanges it for a short-lived credential accepted by the target cloud. Google positions Workload Identity Federation for external workloads and describes it as an alternative to distributing service-account keys.
The exchange does not remove authorization work. You still need to bind the resulting principal to specific IAM roles, resource policies, or conditions. Check the token’s issuer, subject, audience, lifetime, and intended target before allowing impersonation or resource access.
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How do I connect an AI agent securely, step by step?
Use this sequence for a new integration. Complete it for each environment, agent, and target service rather than copying one broad credential across a fleet.
- Map the call path. Record where the agent executes, which cloud APIs or resources it must reach, whether a human is present, and which operations are read-only or mutating. Include intermediate services such as queues, function invocations, and data stores.
- Choose a provider-supported mechanism. For a supported in-cloud runtime, evaluate managed identity or an attached workload identity. For an external runtime calling Google Cloud, evaluate Workload Identity Federation before creating a service-account key. For a user-facing assistant, select the provider’s delegated flow.
- Create a dedicated principal. Assign a separate identity to the agent, workload, or environment. Microsoft’s Entra Agent ID guidance supports unique identities per agent instance where that capability is available; Google recommends application-specific service accounts in federation designs. Do not share one principal among unrelated agents, development and production, or human and machine activity.
- Define the minimum permission set. List exact resources and operations, then grant resource-level roles or equivalent permissions. Add IAM conditions, permission boundaries, or other policy constraints where the provider supports them. Start with read-only access when possible and add each write operation deliberately.
- Keep the exchange outside the model context. Put token acquisition in the runtime, tool gateway, or service layer that enforces policy. Pass the agent only the tool result or a controlled error, not the access token, refresh token, private key, or client secret. This is an architectural safeguard: a particular agent framework should not be assumed to isolate secrets automatically.
- Bind each tool to an allowed audience and resource. Reject requests for an unexpected API, tenant, project, subscription, account, or region. In federation, make subject and audience mappings specific enough that an assertion from one repository, branch, workload, or environment cannot impersonate another.
- Instrument and test. Log the principal, user (when delegated), target resource, operation, authorization result, and correlation ID. Test expired tokens, denied permissions, revoked identities, wrong audiences, and attempts to access a resource outside the agent’s assignment before production.
- Define revocation and ownership. Name the team responsible for the identity, document how to disable it, and rehearse the response to a suspected compromise. Review role changes and token use continuously instead of waiting for an annual access review.
These controls align with the AWS agent-security principles for verifiable communication, separate human and agent permissions, least privilege, short-lived credentials, and auditable identity. The AWS document is a set of security principles, not a universal setup recipe for every AWS compute service or agent framework.
How should I grant permissions to an agent?
Model permissions around the agent’s job, not around the cloud product’s largest convenient role. A document summarizer may need object reads from one bucket and no delete, key-management, or account-administration rights. A deployment agent may need a narrow release role in one project but not permission to create new identities.
- Scope by resource: Name the specific bucket, queue, database, project, subscription, account, or API collection.
- Scope by operation: Separate read, create, update, delete, invoke, and policy-management actions.
- Constrain by context: Use conditions such as environment, resource tags, network location, or approved workload claims where supported.
- Separate duties: Keep identity administration, billing, key management, and production deployment outside a general-purpose agent unless those functions are its explicit, separately controlled role.
- Use different principals: Development, staging, and production agents should not share an identity. Distinct principals make logs attributable and let you revoke one environment without stopping another.
Authentication and authorization must be reviewed together. A correctly federated external workload can still be dangerous if its IAM binding permits broad impersonation or if a resource policy accepts every principal from a large identity pool.
How do I keep tokens and keys out of prompts, code, and configuration?
Treat every model-visible channel as untrusted. Never place a bearer token, refresh token, service-account key, private key, or client secret in a system prompt, tool description, conversation transcript, source repository, issue ticket, or plain configuration file.
A safer architecture has three layers:
- Model layer: chooses an approved operation and supplies business parameters.
- Policy and tool layer: validates the operation, resource, user context, and limits, then requests a token from the identity mechanism.
- Cloud request layer: attaches the short-lived credential and sends the call to the target API, returning only the permitted result.
Redact authorization headers and token-like values from application logs, traces, error messages, and tool outputs. Store only the minimum metadata needed for investigation. Google’s service-account-key guidance warns that possession of a key enables authentication as that service account and can lead to privilege escalation; eliminating the key is preferable to trying to hide it from the model.
What are the Microsoft Azure and Entra considerations?
For an agent running on Azure, use a managed identity when the compute runtime and target service support Microsoft Entra authentication. The platform can obtain Entra tokens for the workload without developers distributing credentials. Verify the target API’s accepted token type, tenant, audience, and required roles before deployment.
For a user-facing agent, use delegated permissions and an on-behalf-of exchange for downstream APIs where appropriate. Keep the user’s authorization in the chain and return an authorization error when the user lacks access; do not silently fall back to a highly privileged application identity.
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Microsoft’s Entra Agent ID best practices describe governance concepts such as unique identities per agent instance and blueprint-level controls. Availability depends on the Entra capability and runtime you have adopted, so treat Agent ID features as provider-specific rather than a universal requirement.
What are the Google Cloud considerations?
Google Cloud can represent workloads with service accounts and, in supported environments, managed workload or agent identities. An agent running outside Google Cloud should generally be evaluated for Workload Identity Federation. The external identity provider’s assertion is exchanged for short-lived Google credentials, avoiding the operational burden and leakage risk of distributing a service-account key.
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Service-account keys remain a possible fallback, not a default. If a target or legacy integration leaves no federation option, apply the key controls described below and plan a migration path.
What does the AWS guidance cover?
The reviewed AWS Agentic AI Lens focuses on principles rather than a single implementation recipe. It calls for verifiable authentication on every agent-to-agent and agent-to-service communication, separate permissions for agents and humans, least privilege, short-lived credentials, and auditable identity.
Translate those principles into the AWS runtime you actually use: identify the workload principal, bind it to the smallest set of IAM actions and resources, avoid passing credentials through the model, and monitor both token use and policy changes. The exact configuration depends on the AWS compute service, target API, and agent framework; do not assume one identity object or sample policy works everywhere.
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How do I connect agents across clouds or multiple runtimes?
Do not assume an identity object from one provider will be understood by another provider or API. Define an explicit trust relationship for every hop: external issuer, token audience, subject mapping, target resource, and authorization policy.
For example, an agent hosted outside Google Cloud might federate into Google for a storage call while using a separate identity mechanism for an Azure API. Keep those identities distinct unless there is a documented reason to bridge them. A shared cross-cloud credential obscures attribution and expands the blast radius of a leak.
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Validate each hop independently in a non-production environment. Test that a token minted for one cloud cannot be replayed against another audience, that a staging identity cannot reach production, and that a denied authorization is visible in logs rather than retried with a broader principal.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should I do if a static key is unavoidable?
First confirm that the target service or legacy library cannot use a managed identity, federation, delegated token, or another short-lived mechanism. If a static key is genuinely required, treat it as a high-risk exception with an owner, expiration plan, and documented replacement target.
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- Create a principal used only by that integration; never reuse a human key or a key shared by unrelated agents.
- Grant the smallest resource and operation scope possible.
- Keep the key in a dedicated secret-management system with access controls and audit logging, not in source code or a model prompt.
- Prevent the key from entering logs, traces, crash reports, support tickets, or tool results.
- Set a rotation schedule, test the replacement procedure, and record how to revoke the old key immediately.
- Monitor for unexpected locations, APIs, resources, or volumes of use.
Google notes that anyone possessing a service-account key can authenticate as that account. A narrow role limits what the key can do, but it does not make possession harmless; revocation and rotation remain essential.
What failures should I expect, and how do I fix them?
The token exchange succeeds, but the API returns permission denied
Authentication worked; authorization did not. Inspect the principal actually present in the token, the target audience, the resource-level policy, and the required role or scope. Add only the missing permission after confirming the request is legitimate.
The agent can access another environment
Look for a shared principal, broad identity-pool mapping, wildcard resource policy, or a federation condition that does not distinguish branches, repositories, projects, or tenants. Split the identities and tighten subject and audience conditions.
A user can do something in the console, but the agent cannot
The agent may be using app-only access, a different API scope, or a token for the wrong audience. If the task is user-delegated, pass the user context through the supported delegated flow rather than granting the agent a larger app role.
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Remove authorization headers and token fields at the logging boundary, scrub exception messages, and ensure the tool returns data rather than credentials. Rotate any exposed secret before investigating further.
Revoking an agent does not stop all calls
Check for cached tokens, duplicate identities, scheduled jobs, or another runtime still using the same principal. Disable every associated identity and credential, then verify from audit logs that new requests are denied.
How do I audit and operate agent identities over time?
Identity security is an operating process, not a one-time token configuration. Maintain an inventory that ties each agent identity to an owner, runtime, environment, target resources, permission rationale, and revocation procedure.
- Review role and policy changes, not only login events.
- Alert on a new audience, unusual resource, unexpected region, or access outside the agent’s schedule.
- Track delegated calls with both the human subject and the application or agent principal.
- Use short-lived credentials where the platform supports them and monitor token issuance and failure rates.
- Revalidate permissions when the agent’s tools, prompts, data sources, or deployment environment change.
- Disable unused identities quickly and remove stale federation bindings.
Microsoft recommends monitoring token usage and auditing permissions, while AWS emphasizes ongoing access review and drift validation. Google’s federation guidance similarly treats trust mappings and impersonation grants as controls that require continuing review.
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- Is the agent hosted on the same cloud as the resource, and do both sides support a managed identity?
- If not, can the hosting platform authenticate to a trusted external identity provider for federation?
- Is a signed-in person the authority, or is the agent performing autonomous work?
- What exact resources and operations are required, and which can remain read-only?
- Can the identity be unique to this agent instance, environment, and deployment?
- Where are token acquisition, policy checks, redaction, and logging enforced outside the model?
- How will you detect misuse, rotate an exception key, or revoke the identity during an incident?
The strongest default is a dedicated, short-lived workload identity with narrowly scoped authorization and clear audit attribution. Deviate only when the runtime or target service requires another supported pattern, and document the trade-off.
Frequently Asked Questions
Can an AI agent use my personal cloud login directly?
It should not. Personal passwords and session cookies are difficult to scope, rotate, and attribute; use delegated OAuth-style access for a signed-in user or a separate workload identity for autonomous work. Keep any human administrator account out of the agent’s runtime.
How long should an agent token remain valid?
Use the shortest lifetime that fits the operation and retry behavior, subject to the cloud provider and API limits. Short-lived credentials reduce the window for replay, but your runtime must handle expiration and obtain a new token without exposing it to the model.
What information belongs in an agent access log?
Record the agent or workload principal, delegated user when applicable, token audience, target resource, requested operation, authorization result, timestamp, environment, and correlation ID. Never log bearer tokens, private keys, or complete authorization headers.
Should every tool in an agent have its own identity?
Give a tool its own principal when its permissions or risk differ materially from the rest of the agent, such as a deployment or deletion tool. Otherwise, a dedicated identity per agent instance can work if policy checks constrain each tool and the permissions remain narrowly scoped.
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