Keep every Bedrock tool behind a dedicated execution boundary: separate the caller, Bedrock’s service-assumed role, and the runtime or Lambda execution identity; grant each only the permissions it needs; and prevent deployment credentials, unrelated source code, files, and network destinations from entering the runtime. For existing Bedrock Agents Classic action groups, that means a narrowly scoped Bedrock service role plus a separately scoped Lambda execution role and Lambda resource policy. For new implementations, follow the corresponding AgentCore identity and runtime model instead.
What isolation means for a Bedrock agent
Isolation is not a single IAM switch. It is a set of independent boundaries that limit what an agent can call, what code can run, which data the code can reach, and which identity pays for those calls. A useful design treats the model as an untrusted decision-maker that can request a tool, not as a principal that should inherit an administrator’s credentials.
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- Identity boundary: the identity that invokes an agent is not the identity that Bedrock assumes, and neither is the identity used by a Lambda function or AgentCore runtime.
- Permission boundary: each identity has only the model, schema, data, API, and runtime permissions required for its task. AWS’s preventative guidance says custom policies should grant only the permissions required to perform a task (AWS preventative security best practice for agents).
- Code and data boundary: the tool receives only the handler code, libraries, files, secrets, and data sources that its operation needs. Deployment credentials and unrelated repositories stay outside the runtime.
- Network boundary: outbound destinations are limited to the APIs and services the tool is meant to call.
- Action boundary: a tool that can change an account, send a message, move money, or alter data performs its own authorization checks and, where required, waits for human approval. A natural-language plan from the model is not authorization.
These layers matter together. A narrowly scoped IAM role cannot compensate for a runtime that contains production credentials, and a sandboxed runtime cannot make an over-broad role safe.
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Choose the correct Bedrock implementation path
AWS documentation currently distinguishes the original action-group product from the newer AgentCore direction. The action-group page calls the original product Bedrock Agents Classic, says it is no longer open to new customers, and points readers toward AgentCore for similar capabilities (AWS action groups documentation). Existing customers can continue to use Classic, but a new design should not assume that every Classic role or Lambda detail maps directly to AgentCore.
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| Decision point | Agents Classic | AgentCore |
|---|---|---|
| Who should use it? | Existing customers maintaining Bedrock agents with action groups. | New implementations following AWS’s current direction for similar agent capabilities. |
| Primary identity layout | A Bedrock service role plus a Lambda function resource policy and a separate Lambda execution role. | Distinct caller, service-assumed, and execution-environment identities. |
| Execution boundary | Business logic runs in an action-group Lambda function. | Business logic runs in a configured AgentCore execution environment. |
| What must be scoped? | Model inference, action-group schemas, optional knowledge-base or guardrail features, Lambda invocation, and the Lambda’s downstream APIs. | Runtime and runtime-endpoint access, files, network destinations, tools, credentials, and downstream APIs. |
| High-impact operations | Authorization belongs in the Lambda and any downstream service; add human review where the operation requires it. | Authorization and required human review belong in the application or tool implementation, independently of the model’s choice. |
The three identities you should keep separate
AWS’s AgentCore security guidance describes three identities in a session: the caller, the role assumed by the service, and the execution-environment identity (AWS security and IAM roles). The same separation is a useful mental model for Classic even though the concrete resources differ.
Caller identity
This is the human, application, or CI workload that starts an agent session or creates a runtime. It should be allowed to invoke only the intended agent or runtime and to perform only the session-management operations it needs. It should not automatically receive the permissions used by a tool to read a database or modify a production resource.
Service-assumed identity
Bedrock or AgentCore assumes this role to perform service-side work. In Classic, it commonly covers foundation-model inference and access to action-group API schemas, with additional statements only when enabled features require them. In AgentCore, the service role is one of the three identities and should not be merged with the runtime’s application identity.
Execution-environment identity
This is the role used by the Lambda handler in Classic or by the AgentCore runtime environment. It should contain only the downstream permissions implemented by that tool: for example, access to one queue, one table, or one internal API operation rather than broad account administration. It is the identity most likely to be abused if a tool has a code or dependency vulnerability, so do not attach deployment or operator permissions to it.
“Use separate IAM roles for these tasks.” — AWS documentation, Security and IAM roles
Agents Classic: isolate an action-group Lambda
The following procedure applies to existing Bedrock Agents Classic deployments. The names and optional statements should be checked against the current AWS console and documentation for the features enabled on your agent.
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Inventory exactly what the agent uses
List the foundation model, action-group API schemas, knowledge bases, guardrails, data stores, and any other Bedrock features attached to the agent. Separate required features from features that were enabled experimentally. The service role should contain statements only for features in active use; AWS identifies model inference and action-group schema access as common permissions and treats knowledge-base and other feature permissions as conditional (AWS service-role guidance).
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Create a dedicated Bedrock service role
Create a role used only by this agent or by a tightly related group of agents. Its trust relationship must allow the Bedrock service to assume it. The documented trust example also uses source-account and source-ARN conditions. During initial creation, AWS examples may use a wildcard for the agent identifier; after the agent exists, replace that wildcard with the specific agent ID so another agent cannot use the same trust relationship (AWS service-role guidance).
Do not attach administrator, deployment, source-control, or operator policies to this role. Keep optional statements out when the corresponding feature is not enabled. If the agent later gains a knowledge base or another feature, add the smallest documented statement needed for that feature and review the trust and resource scope at the same time.
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Give the service role only service-side permissions
Attach the minimum permissions needed for the model and the configured agent features. The role is not a substitute for the Lambda execution role: it should not be granted the database, ticketing, file-system, or internal-API permissions that belong to the action handler.
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Create a separate Lambda execution role
The action-group function’s execution role should contain the downstream operations implemented by that function and nothing else. If one action reads a reporting table and another sends an approval request, use separate functions or separate narrowly scoped roles when practical. Keep deployment credentials, package-repository tokens, administrator sessions, and unrelated environment variables out of the function configuration.
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Add the Lambda resource-based policy
Classic action-group invocation has two policy boundaries. The Bedrock service role governs what Bedrock can do as a service; the Lambda function’s resource-based policy governs whether Bedrock may invoke that particular function. AWS requires the function policy for an action group and documents optional condition keys for further restriction (AWS Lambda action-group configuration).
Set the policy’s resource to the relevant function rather than a broad family of functions. Use the documented service principal and, where applicable, condition keys that bind the permission to the intended account, agent, or source ARN. A role policy alone does not authorize the invocation, and a permissive function policy does not grant the handler access to its downstream systems.
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Validate the event and response contract
Bedrock sends action parameters and related metadata in the Lambda event. Parse and validate every field, enforce allowed values and lengths, and reject unexpected actions before calling a downstream service. Return the structured response format required by the action-group contract; do not pass arbitrary model text directly into a privileged API.
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Keep the deployment path separate
The identity that packages or updates the Lambda may need write access to deployment artifacts and infrastructure. That identity must not be placed in the function’s runtime environment. Use a deployment role or pipeline identity for release operations and a runtime role for business operations, with no shared long-lived access keys.
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Test the negative cases
Invoke the agent with a request that should use the tool, one that should be refused, and one containing an unexpected parameter. Confirm that the Lambda can perform only its intended downstream operation, that an unrelated API call fails with an authorization error, and that the resource-based policy does not allow an unintended function or source.
AgentCore: isolate the execution environment
For AgentCore, apply the same principle with the product’s runtime model rather than copying a Classic Lambda policy. The security guidance recommends separate roles for the caller, the service, and the execution environment, each with only the permissions required (AWS security and IAM roles).
Scope the caller to the intended runtime
Invocation and session-stop permissions should be scoped to the specific Runtime and Runtime endpoint ARNs. Avoid account-wide resource wildcards when the caller needs only one environment. If several environments exist, separate development, staging, and production resources so a caller authorized for one cannot start or stop sessions in another.
Keep service and runtime permissions distinct
The role assumed by the service should not be reused as the runtime’s application role. The runtime role should contain only the APIs, data stores, queues, files, and other resources that the tools actually call. A service-side permission to manage a session is not a reason to grant the application permission to edit production data.
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AWS’s security page includes preview-specific instructions for authentication and Signature Version 4. Those instructions refer to using an AWS credential profile or the standard provider chain, temporary-credential session tokens, and an iam:PassRole permission for the caller when creating a session (AWS security and IAM roles). Preview behavior and exact policy examples can change, so verify the current page before copying authentication code or granting iam:PassRole. Scope that permission to the exact role the caller is allowed to pass; never use it as a general delegation mechanism.
Contain the runtime, not just the IAM role
Expose only required files and code
Build a small deployment artifact containing the tool and its necessary dependencies. Do not mount a production source tree, infrastructure repository, local credential directory, or shared build workspace into the runtime. Remove test fixtures and configuration files that contain connection strings or unrelated customer data. If a tool needs a template or schema, ship that specific file rather than the directory that happens to contain it.
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Use short-lived, task-specific credentials
Prefer the runtime’s role and temporary credentials supplied by AWS over embedded access keys. Do not copy a developer profile, CI secret, or production administrator token into environment variables, source files, container layers, or logs. Rotate and revoke any credential that has appeared in a tool package or diagnostic output, then inspect the package and build process to prevent recurrence.
Restrict network destinations
Allow outbound connections only to the services required by the tool. A function that writes to one internal API should not have unrestricted internet access simply because a dependency might need it. Where the architecture supports it, use private connectivity and explicit egress controls, and log denied destinations as signals of unexpected behavior.
Publish only the tools the task needs
An agent does not need a catalog of every internal function. Expose separate action groups or runtimes for materially different trust levels, such as read-only reporting and production changes. Keep administrative, migration, and destructive operations out of the general-purpose tool set.
Put authorization at the point of effect
For an operation with an external effect, the tool must re-check the caller, target, scope, and current state immediately before performing it. Add a confirmation or human-review step where organizational policy requires one. The model’s selection of an action is an input to the application, not proof that the action is approved.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Treat prompts, documents, web pages, and tool output as untrusted
Agent workflows often retrieve instructions from documents, websites, tickets, or another tool. Those contents can contain prompt-injection text that attempts to make the model reveal secrets, call a different function, or weaken a control. Do not treat retrieved prose as an IAM grant or as a trusted instruction.
- Keep authorization checks in application code and downstream services rather than asking the model to enforce them.
- Pass only the minimum retrieved content needed for the task and label its origin clearly in the application’s data model.
- Validate tool arguments against an allowlist, type, length, tenant, and resource ownership before making an API call.
- Never include credentials, secret values, or unrestricted internal configuration in model context.
- Record the requested action, effective identity, target resource, authorization decision, and result so an operator can reconstruct a suspicious run.
IAM scoping remains necessary even when an agent uses guardrails or other model-safety features. Those controls address different failure modes and do not replace application-side authorization.
Verification checklist before production
| Check | Expected result | If it fails |
|---|---|---|
| Role separation | Caller, service-assumed role, and execution role are distinct; Classic Lambda has its own execution role. | Split the policies and remove permissions that belong to another identity. |
| Trust relationship | Only the intended Bedrock service can assume the Classic role, with source conditions and a specific agent ID after creation. | Replace broad principals or wildcard agent identifiers and retest assumption. |
| Feature permissions | Model, schema, knowledge-base, guardrail, and other statements match enabled features. | Remove unused statements; add only the documented permission required by the feature that is actually enabled. |
| Lambda invocation | The function resource policy names the intended function and permits the intended Bedrock invocation. | Check the resource, principal, source conditions, and region; a role policy alone is insufficient. |
| Runtime contents | No deployment keys, developer profiles, unrelated source, or production secrets are present. | Rebuild the artifact and rotate exposed credentials. |
| Network egress | Only documented downstream destinations are reachable. | Remove broad egress and investigate denied-connection logs. |
| High-impact action | The tool independently authorizes the request and requires review when policy demands it. | Move the decision into the application or downstream service; do not rely on the model’s text. |
| Negative tests | Unrelated reads, writes, function invokes, and session operations are denied. | Trace the effective policy and resource policy, then reduce the statement to the exact resource and operation. |
Common failure modes and fixes
The agent can invoke the model but not the action group
Check both boundaries: the Bedrock service role and the Lambda function’s resource-based policy. Confirm that the function policy names the actual function ARN and that its principal and conditions match the Classic action-group invocation documented by AWS (Lambda action-group configuration).
The Lambda works in development but exposes production data
The runtime role or packaged credentials are broader than the developer intended. Inspect attached policies, environment variables, container layers, and the build workspace. Replace the role with one limited to the exact production operation, remove copied credentials, and rotate anything that was exposed.
A trust policy works for every agent in the account
The trust relationship still contains a wildcard agent identifier. After the agent is created, bind the source ARN to that specific agent as AWS recommends (Create a service role for Bedrock Agents).
A new project is copying Classic instructions
Stop and determine whether the project is an existing Agents Classic customer or an AgentCore implementation. Classic’s Lambda resource-policy pattern is not a substitute for AgentCore’s caller, service, and execution-environment roles. Start from the current AgentCore security documentation and scope runtime and runtime-endpoint permissions to the resources being deployed.
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A tool follows instructions embedded in a document
Assume prompt injection. Treat the document as data, validate the requested operation in application code, and require the tool’s authorization check before any external effect. Narrow IAM permissions so a successful injection still cannot reach unrelated resources.
A practical operating model
Maintain an inventory for each agent or runtime that names its caller, service role, execution role, tools, downstream resources, files, and network destinations. Review that inventory whenever a model, action group, schema, dependency, or data source changes. Keep development, staging, and production identities and runtimes separate, and make policy changes through the same controlled deployment process as application code.
Use logs to answer four questions for every tool call: who initiated the session, which service and execution identities were effective, what exact resource was targeted, and whether the application authorized the operation. Alert on denied calls to sensitive resources, unexpected destinations, attempts to use deployment-only APIs, and changes to trust or resource-based policies.
For existing Classic agents, the minimum safe pattern is a dedicated Bedrock service role, a dedicated Lambda execution role, a function resource policy restricted to that function, and application-level validation and authorization. For new work, implement the equivalent separation with AgentCore’s three-role model and a constrained execution environment. In both cases, isolation succeeds only when IAM, runtime contents, network access, and tool-side authorization all enforce the same narrow task boundary.
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