Give each AI agent a dedicated AWS IAM role, start that role with no permissions, and add only the API actions and resource ARNs required for its defined tasks. Issue the role through AWS Security Token Service (STS) with short-lived credentials, narrow individual sessions with session policies when useful, and place independent ceilings around the role with a permissions boundary and, where applicable, an AWS Organizations service control policy (SCP). Conditions such as region, resource tags, time, and source VPC further restrict when a permitted call can succeed.
This layered design keeps an agent from becoming a surrogate for a human administrator. It also produces an audit trail that identifies the workload, lets you compare intended access with actual CloudTrail activity, and gives IAM Access Analyzer evidence for tightening the policy over time.
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Start with three separate identities
Least privilege is easier to enforce when each actor has its own identity. An agent workload, the person operating it, and a managed agent service are different principals with different responsibilities.
The agent workload role
An IAM role is an AWS identity with a permissions policy. When the role is assumed, AWS returns temporary credentials for the resulting session. Attach the role to one agent or to a tightly bounded workload, not to every automation process in an account. A dedicated role means you can see which calls came from the agent, revoke or modify its access without disrupting unrelated applications, and avoid inheriting a developer’s broad permissions.
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The human operator identity
People should use federation and temporary credentials rather than long-lived IAM-user access keys. AWS points organizations toward IAM Identity Center for centralized human access. The operator can request or initiate an agent task, but the agent should not simply assume that person’s full role. If an operation must be authorized on the person’s behalf, pass verifiable user context to the downstream service so it can apply user-level rules without giving the agent every permission the person has.
A managed agent platform’s service role
Services such as Amazon Bedrock Agents use a service role to call configured AWS features. That role is not the same as the role used by your application or a human administrator. Scope each role for the calls it is responsible for, and give an action-group function its own resource-based permission when the service must invoke it.
Define the agent’s task before writing a policy
Write a short capability statement first: what the agent may do, in which account and Region, against which resources, and under what trigger. AWS’s Well-Architected Agentic AI Lens says to start an agent role with no permissions and add only what the specific task requires. This is more secure than attaching a broad convenience policy and trying to discover excess access later.
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Turn every planned tool call into an IAM action. “Read the daily invoices” might require a read operation on a particular S3 prefix; “update a ticket” might require one write action on a specific API resource. Do not translate a business verb into an entire service. A role that only reads objects should not receive an administrative S3 policy, and an agent that updates one DynamoDB table should not receive access to every table in the account.
Map each action to a resource ARN
Use the narrowest resource ARN the service supports. Separate input and output locations, tables, queues, secrets, and keys where possible. Avoid * in the resource field as a convenience. Some AWS actions genuinely require a wildcard resource; in that case, limit the action set and add conditions that constrain the request.
Separate read, write, and destructive operations
Read-only discovery, creation or update, and deletion should be distinct decisions. Keep destructive actions out of the base role unless the workflow cannot function without them. A safer design is to use a separate approval path or just-in-time elevation for an exceptional operation, then issue a short session for that transaction.
Illustrative identity policy
The following is a pattern, not a drop-in policy. Replace the example resources with the ARNs for your workload and verify that each service supports the condition keys you choose.
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{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "ReadTaskInputs",
"Effect": "Allow",
"Action": ["s3:GetObject"],
"Resource": "arn:aws:s3:::agent-task-bucket/input/*"
},
{
"Sid": "WriteTaskResults",
"Effect": "Allow",
"Action": ["s3:PutObject"],
"Resource": "arn:aws:s3:::agent-task-bucket/output/*"
},
{
"Sid": "ReadTaskRecord",
"Effect": "Allow",
"Action": ["dynamodb:GetItem"],
"Resource": "arn:aws:dynamodb:us-east-1:123456789012:table/AgentTaskState"
}
]
}The policy deliberately omits listing, deletion, bucket administration, table writes, and unrelated resources. Add an action only after confirming that the task needs it and that the resource scope is correct.
Make the trust policy as narrow as the permissions policy
The permissions policy answers what the role can do. The role’s trust policy answers who may assume it. Allow only the intended workload identity, such as a specific compute service, deployment role, or managed AWS service. Do not use a broad principal merely because it makes an integration work. Check external ID, source account, source identity, or other trust conditions when the integration supports them.
Keep deployment and runtime responsibilities separate. A CI/CD role that creates or updates the agent role should not automatically be the runtime principal that uses data. This separation limits the impact of a compromised agent and makes CloudTrail events easier to interpret.
Issue temporary credentials and narrow each session
Use AWS STS to obtain temporary credentials whenever the agent runs. Avoid embedding long-lived access keys in prompts, code, containers, or tool configuration. The role policy defines the maximum set of actions available to its credentials; an individual AssumeRole call can narrow that set further with a session policy.
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The effective permissions of an assumed-role session are the intersection of the role’s identity policy and the session policy. A session policy can remove an allowed action for one run, but it cannot add an action the role does not allow. AWS states: “You cannot use session policies to grant more permissions than those allowed by the identity-based policy of the role that is being assumed.”
For example, a role might allow an agent to read and delete objects in a controlled workflow. A session created for an analysis task can pass a policy that allows the same read operations but excludes s3:DeleteObject. If the role never allowed access to a second bucket, no session policy can create that access.
Use a session policy for transaction-level limits
Generate the session policy from the current job rather than from untrusted natural-language text. Restrict it to the resource set, operation type, or task phase known by your orchestrator. Validate the policy before calling STS and log the requested scope with the job identifier. Treat the role policy as the durable baseline and the session policy as a temporary reduction.
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Keep sessions short
AWS’s Agentic AI Lens recommends 15-to-60-minute sessions for agent credential issuance. That range is guidance, not a measured security statistic or a universal mandate. Select a duration that covers one unit of work, then renew deliberately for a still-authorized task. Short sessions reduce the useful lifetime of leaked credentials and make idle credentials less valuable.
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If an agent occasionally needs a privilege outside its normal role, do not permanently add that privilege. Use IAM Identity Center or a custom access-request workflow to obtain approval and issue a narrowly scoped, short-lived session. Record who approved it, which operation was requested, and when the elevation expires.
Add independent permission ceilings
Layered controls prevent a mistaken role edit from becoming unlimited access. These controls complement a carefully scoped identity policy; they do not replace it.
| Control | What it limits | How to use it |
|---|---|---|
| Role identity policy | The API actions and resources the agent can use | Grant only task-required actions on specific ARNs. |
| STS session policy | The subset available during one assumed-role session | Pass a policy for the current transaction; it can only reduce the role’s permissions. |
| Permissions boundary | The maximum permissions identity-based policies can grant to the role | Attach a boundary that prevents future role-policy changes from exceeding an approved ceiling. |
| SCP | The maximum permissions available to IAM users and roles in an AWS Organizations account | Deny organization-wide or account-wide capabilities that agents must never use. |
| IAM condition | When, where, or in which context an allowed request may occur | Constrain region, tags, time, VPC, or other supported request attributes. |
| Downstream user context | The originating person’s authorization for a user-initiated operation | Pass verifiable context to the resource instead of giving the agent the person’s complete role. |
Permissions boundaries
A permissions boundary is a maximum on what identity-based policies can grant to a role. If a later policy edit tries to add an action outside the boundary, the role still cannot exercise it. Put the boundary under controlled administration so the same principal that edits the agent policy cannot silently remove the ceiling.
Service control policies
An SCP sets a maximum for IAM users and roles in accounts governed by AWS Organizations. Use it for capabilities that should remain unavailable to agent accounts, such as specified Regions or high-impact account operations. An SCP does not grant access; the role policy must still allow the task. Check how SCPs, boundaries, identity policies, resource policies, and explicit denies combine for the principal and resource in your design.
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Constrain requests with context conditions
When an action and resource are correct but the context matters, add conditions to the allow statement. AWS’s Agentic AI Lens highlights several useful dimensions.
Region
Use aws:RequestedRegion to keep calls in the Regions where the workload operates. Confirm that global services and control-plane actions handle this key as expected before relying on it.
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Resource tags
Require a tag such as Environment=agent or Owner=finance-automation when the service supports tag-based authorization. Tag conditions are effective only if creation and tag changes are themselves controlled; otherwise an agent might retag an unrelated resource to satisfy the condition.
Time windows
Use aws:CurrentTime for scheduled or approval-bound work. A time condition is a backstop, not a replacement for short STS sessions, because clock boundaries and long-running operations need careful testing.
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Network origin
Use aws:SourceVpc where the service and request path provide that context. Validate the actual network path, including private endpoints and service-to-service calls, so a condition does not block legitimate traffic or create a false sense of isolation.
User and transaction context
For a user-initiated action, carry a signed or otherwise verifiable user and transaction identifier to the downstream authorization layer. The downstream service can then decide whether that person may access the particular record. Do not solve this problem by letting the agent assume the user’s entire IAM role.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Bedrock Agents: scope both the service role and action groups
A custom Amazon Bedrock Agents deployment illustrates why least privilege has more than one policy surface. The agent service role needs a trust policy and identity permissions that match the features enabled in that agent. Start with only the features you actually configure.
Base permissions to evaluate
- Model access needed by the configured foundation model or inference path.
- S3 access to objects that contain action-group OpenAPI schemas.
- Access to attached knowledge bases and the data paths they require.
Optional capabilities can add permissions for collaborators, provisioned throughput, guardrails, code interpretation, or KMS decryption. Do not copy every optional permission into a deployment that does not enable the corresponding feature. Re-check the current Bedrock Agents documentation when features, Regions, or service integrations change.
Action-group Lambda permission
The Lambda function used by an action group also needs a resource-based policy statement allowing the Bedrock service role to invoke it. Grant invocation to the specific function and intended service principal; granting the role identity permission alone does not create the function’s resource-based authorization. Remove the statement if the action group is retired.
Prefer operation-specific Bedrock access
AWS identity-based examples include narrower alternatives for identities that use Bedrock Agents instead of attaching the broad AmazonBedrockFullAccess convenience policy. Adapt those examples to the features and account configuration you actually use, and verify every action against CloudTrail before production rollout.
Observe, test, and tighten from real activity
Least privilege is an operating process, not a one-time JSON exercise. Start with a small role, exercise every supported agent path, and inspect what it actually called.
- Deploy in a non-production account or isolated resources. Use representative data and deny destructive operations while validating the workflow.
- Exercise success and failure paths. Test normal reads and writes, missing inputs, retries, malformed tool arguments, expired credentials, and attempts to reach an unrelated resource.
- Review CloudTrail events. Confirm the principal, action, resource, Region, source context, and session name for each call. Investigate any call that was not part of the task statement.
- Generate a candidate policy with IAM Access Analyzer. Its CloudTrail-based policy suggestions can reveal actions observed during a defined period. Treat the output as a starting point, not an automatic approval: observed activity may omit rare but required paths and may include calls caused by a misconfiguration.
- Rewrite for narrower resources and conditions. Replace wildcards where the service supports resource-level permissions, split read and write statements, and add appropriate context keys.
- Re-test after every change. A denied call may indicate a missing legitimate permission, an incorrectly scoped ARN, or a condition that does not match the real request path.
- Schedule access reviews. Compare current policy statements with recent CloudTrail activity and the agent’s changed capabilities. Remove permissions for retired tools, buckets, tables, schemas, and functions.
Common failure modes and fixes
“The agent needs administrator access to work.”
Usually this indicates an undefined task boundary or a missing resource ARN. Capture the denied action from CloudTrail, decide whether the operation is truly required, and add only that action for the smallest supported resource. If the operation is exceptional, use an approval workflow rather than expanding the permanent role.
“The session policy did not grant the permission I added.”
Session policies cannot elevate a role. Add the required action to the role policy first, then use the session policy only to narrow that role for the transaction.
“The role policy allows Lambda invocation, but Bedrock receives AccessDenied.”
Check the Lambda function’s resource-based policy. The action-group function must explicitly allow the Bedrock service role to invoke it, in addition to any identity-based permission required by the integration.
“A condition blocks calls that look correct.”
Inspect the actual request context in CloudTrail and verify that the service supports the condition key for that action. Test global-service calls, private endpoint paths, cross-account access, and tag propagation rather than assuming every request supplies the same keys.
“The policy suggestion contains too much access.”
CloudTrail-based generation reflects observed calls, not an approved design. Remove calls caused by setup, debugging, or accidental access; narrow resources and conditions; then test rare workflows separately before accepting a statement.
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“A human’s access was accidentally inherited.”
Separate the agent role from the operator’s identity and inspect trust policies for paths that let the agent assume human roles. Pass verifiable user context to downstream authorization instead of delegating the complete human permission set.
Pre-deployment checklist
- The agent has a dedicated runtime role and a narrowly scoped trust policy.
- The role began with no permissions, and every allow statement maps to a documented task.
- Actions use the smallest supported resource ARNs; destructive operations are separated or excluded.
- STS supplies temporary credentials, with a session duration appropriate to the job and within AWS’s 15-to-60-minute agent-session guidance where practical.
- Session policies narrow individual transactions and are never treated as grants beyond the role.
- A permissions boundary caps future role-policy changes.
- SCPs deny capabilities that must remain unavailable in the agent’s account or organization.
- Region, tag, time, and source-VPC conditions are used only where the request path and service support them.
- User-initiated operations carry verifiable user context rather than the user’s full role.
- For Bedrock Agents, the service role includes only enabled features, and each action-group Lambda has the required resource-based invocation permission.
- CloudTrail tests cover normal, denied, expired, malformed, and exceptional paths.
- IAM Access Analyzer suggestions have been manually reviewed, and a recurring drift review is scheduled.
What a durable least-privilege design looks like
The durable pattern is layered: a role policy defines the agent’s narrow capability, STS makes its credentials temporary, a session policy reduces one transaction, conditions bind calls to the right context, and boundaries or SCPs stop later changes from exceeding organizational limits. Separate identities preserve accountability, while CloudTrail and IAM Access Analyzer turn real behavior into evidence for the next policy revision. Treat AWS documentation as the authority for changing service requirements, and validate every policy against the exact agent, services, resources, account structure, and Regions in your deployment.
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