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Enforce least-privilege authorization in multi-agent AI chains using Cedar

6 July 2026 at 18:52

If you’re building multi-agent AI systems, you need to prevent authorization scope from silently expanding as agents delegate tasks through multi-hop chains. Without proper controls, an agent can potentially act beyond what the originating user authorized, even when role-based access control (RBAC) policies are in place. The OWASP Top 10 for Agentic Applications classifies this risk as ASI03: Identity & Privilege Abuse.

This post shows you how to address the potential risk using a three-layer policy model built with Cedar, an open source authorization policy language, deployed on Amazon Web Services (AWS). The reference implementation uses OAuth 2.0 for authentication and Cedar for authorization. A trusted identity provider authenticates the originating user, then Cedar policies enforce authorization across three layers using verified token claims.

Reference implementation overview

To enforce authorization at each hop in a multi-agent delegation chain, the reference implementation uses two AWS Lambda functions in sequence. A Model Context Protocol (MCP) adapter Lambda function normalizes inbound requests and cryptographically signs the originating user context. This prevents downstream tampering. A Cedar evaluator Lambda function evaluates three independent policy layers sequentially, halting on the first deny.

Table 1: Three-layer Cedar policy evaluation model

Layer What it checks Principal to resource
L1 – Agent-to-tool Whether the invoking agent has a sufficient trust score (1–5), belongs to the correct namespace (for example, payments), and is in the production lifecycle stage Agent to tool
L2 – Agent-to-agent delegation Whether the delegation hop count is within the hard limit of five, and whether requested tasks are a subset of the target agent’s registered capabilities Agent to agent
L3 – Originating user authorization Whether the human who initiated the chain has the required role (for example, admin), has completed MFA, and is within the allowed delegation depth Agent to tool (user in context)

Architecture

Cedar evaluates authorization but doesn’t establish identity. Before Cedar can evaluate context.originating_user.role or context.originating_user.mfa_verified, a trusted authentication layer must establish the user’s identity and produce verifiable claims. Steps 1–3 handle authentication; steps 4–10 handle authorization. The architecture shown in Figure 1 is described in the following lists:

Authentication (steps 1–3)

  1. The originating user authenticates with an OIDC-compliant identity provider (in this reference implementation, Amazon Cognito with TOTP multi-factor authentication (MFA)). The identity provider (IdP) issues a signed JSON Web Token (JWT) containing claims such as sub, role, amr (authentication methods), and session_id.
  2. Amazon Cognito returns the signed JWT to the user.
  3. The user passes the JWT and task request to the AI agent (MCP client). The agent carries the originating user context in the MCP _meta envelope.

Authorization pipeline (steps 4–10)

  1. The AI agent sends a Model Context Protocol (MCP) request to AWS WAF, which filters using CommonRuleSet, SQLiRuleSet, rate limiting, and body size constraints.
  2. Amazon API Gateway (with Amazon Cognito authorizer) verifies the JWT signature against the user pool’s public keys and rejects invalid or expired tokens. Valid requests are forwarded to the MCP protocol adapter Lambda function, which applies Amazon Bedrock Guardrails content filtering.
  3. The adapter extracts verified claims from the token and maps them to Cedar context attributes:
    1. JWT role claim : context.originating_user.role
    2. JWT amr includes MFA method: context.originating_user.mfa_verified = true
    3. JWT sub: context.originating_user.user_id
    4. JWT sid: context.originating_user.session_id
    5. JWT amr claim: context.originating_user.authentication_method

    The adapter then computes an HMAC-SHA256 signature over the user context (user_id, role, mfa_verified, authentication_method, and session_id in canonical order) using a key from AWS Secrets Manager.

  4. The adapter constructs a signed request envelope and invokes the Cedar evaluator Lambda function.
  5. The evaluator verifies the HMAC-SHA256 signature, retrieves L2 and L3 Cedar policies from Amazon Verified Permissions, and evaluates all three layers (L1, L2, and L3), halting on the first deny.
  6. The evaluator emits an Open Cybersecurity Schema Framework (OCSF) 99001 audit event to Amazon CloudWatch Logs. Failed emissions fall back to an Amazon Simple Queue Service (Amazon SQS) dead-letter queue (DLQ).
  7. Amazon CloudWatch dashboards and alarms monitor evaluation latency, deny rates, and DLQ depth. Alarm notifications route through Amazon Simple Notification Service (Amazon SNS).

Context integrity through delegation hops

Two mechanisms work together to protect identity across hops:

  • Hash-based Message Authentication Code (HMAC-SHA256) ensures integrity and authenticity. Every downstream evaluator verifies this signature before trusting the context.
  • OAuth 2.0 Token Exchange (RFC 8693) sets delegation scope using the on-behalf-of (OBO) pattern. When the orchestrator delegates to a downstream agent (data-bot), it exchanges the original token for a scoped OBO token that records who’s acting on behalf of whom and with what authority. The Cedar policies (detailed in Step 2: Three-layer policies) then check whether that scoped delegation is permitted and verify the originating user claims carried in the OBO token. Token exchange limits each downstream agent to only the delegated task’s scope instead of passing through the full original token. For enterprise deployments, use token exchange alongside HMAC. OAuth tracks who is acting on behalf of whom and with what scope. HMAC verifies that the context hasn’t been tampered with and came from a trusted source.

Prerequisites

The following prerequisites are needed to deploy the reference implementation. Before you begin, clone the repository:

git clone https://github.com/aws-samples/sample-cedar-agentic-ai-authorization.git
cd sample-cedar-agentic-ai-authorization

Verify that you have the following:

Walkthrough

In this walkthrough, you define the Cedar entity schema and policies, deploy the infrastructure with AWS CDK, and integrate your identity provider.

To define the Cedar entity schema

In this step, you define a schema with two entity types (Agent and Tool) and two actions (invoke_tool and delegate_task) in the AgentAuthz namespace. Notice that there is no User entity. Instead, you carry the originating user’s identity in the evaluation context record, which is a structured data object passed alongside each authorization request.

{
  "AgentAuthz": {
    "entityTypes": {
      "Agent": {
        "shape": {
          "type": "Record",
          "attributes": {
            "trust_level": { "type": "Long", "required": true },
            "namespace": { "type": "String", "required": true },
            "registered_capabilities": {
              "type": "Set", "element": { "type": "String" }, "required": true
            },
            "lifecycle_stage": { "type": "String", "required": true }
          }
        }
      },
      "Tool": {
        "shape": {
          "type": "Record",
          "attributes": {
            "namespace": { "type": "String", "required": true },
            "risk_level": { "type": "String", "required": true }
          }
        }
      }
    },
    "actions": {
      "invoke_tool": {
        "appliesTo": { "principalTypes": ["Agent"], "resourceTypes": ["Tool"] }
      },
      "delegate_task": {
        "appliesTo": { "principalTypes": ["Agent"], "resourceTypes": ["Agent"] }
      }
    }
  }
}

This schema is deployed to an Amazon Verified Permissions policy store by the VerifiedPermissionsStack CDK stack. In the reference implementation, the schema file is located at cedar-entity-schema.json.

Agent topology and attributes

The following tables show the agents and tools registered in this reference implementation, along with the attributes the Cedar evaluator function retrieves from the entity store. The test scenarios that follow trace requests through this topology.

Table 2: Agent attributes

Entity Type trust_level namespace lifecycle_stage registered_capabilities
orchestrator Agent 5 orchestration production

delegate_task

route_request

finance-agent Agent 3 payments production

process_payment

refund

data-bot Agent 4 data production

query_records

delete_records

Table 3: Tool attributes

Tool namespace risk_level
process_payment payments medium
delete_records data high
query_records data low

The orchestrator can delegate to both data-bot and finance-agent. Each agent can only invoke tools within its registered capabilities. The test scenarios below trace requests through these delegation paths.

To create three-layer Cedar policies

The following policies are deployed to the same Verified Permissions policy store. In the reference implementation, policy files are located under cedar/policies/ organized by layer: layer1-agent-to-tool/, layer2-agent-to-agent/, and layer3-originating-user-auth/.

Layer 1 (agent-to-tool): This policy permits the finance-agent to invoke the process_payment tool only when three conditions are met: the agent’s trust score is at least 3, it belongs to the payments namespace, and it’s deployed in the production lifecycle stage. If any condition fails, the request is denied. The agent’s trust_level, namespace, and lifecycle_stage aren’t self-reported in a production deployment. Instead, the evaluator retrieves these attributes from the Verified Permissions entity store using the agent_id as a lookup key.

Important: The reference implementation accepts these values from the request payload for simplicity. Production deployments must validate agent attributes against an authoritative source to prevent a compromised agent from escalating its own trust.

The trust_level attribute uses a 1–5 integer scale that represents an agent’s verified maturity: 1 for newly registered and untested agents, 3 for agents that have passed integration testing and security review, and 5 for agents with a proven production track record. Organizations assign trust levels through their agent promotion pipeline, not through self-declaration. The lifecycle_stage attribute (development, staging, production) prevents pre-production agents from invoking production tools, even if they have the correct namespace and trust score.

// L1-001: Finance agent can invoke payment tools
permit(
  principal == AgentAuthz::Agent::"finance-agent",
  action == AgentAuthz::Action::"invoke_tool",
  resource == AgentAuthz::Tool::"process_payment"
) when {
  principal.trust_level >= 3 &&
  principal.namespace == "payments" &&
  principal.lifecycle_stage == "production"
};

Layer 2 (agent-to-agent delegation) enforces depth limits and capability constraints. The orchestrator agent delegates tasks to data-bot only when the delegation chain is three hops or fewer and the requested capabilities are a subset of data-bot’s registered capabilities. A separate forbid policy (L2-004) enforces a hard system-wide limit of five hops regardless of which agents are involved.

// L2-002: Orchestrator can delegate to data agent
permit(
  principal == AgentAuthz::Agent::"orchestrator",
  action == AgentAuthz::Action::"delegate_task",
  resource == AgentAuthz::Agent::"data-bot"
) when {
  context.delegation_depth <= 3 &&
  context.target_capabilities.containsAll(context.requested_capabilities)
};

Layer 3 (originating user authorization) keeps the agent as the principal, but the policy evaluates context.originating_user to validate the human who initiated the request. data-bot invokes the delete_records tool only when the originating user has the admin role, has verified MFA, and the delegation chain is at most two hops deep. Without this layer, an agent with the right capabilities could invoke destructive tools regardless of who initiated the request.

// L3-001: High-risk tool (delete_records) requires admin + MFA
permit(
  principal == AgentAuthz::Agent::"data-bot",
  action == AgentAuthz::Action::"invoke_tool",
  resource == AgentAuthz::Tool::"delete_records"
) when {
  context.originating_user.role == "admin" &&
  context.originating_user.mfa_verified == true &&
  context.delegation_depth <= 2
};

Key design point: The principal remains the agent, not a user entity. The user’s role and MFA status are checked through context attributes, keeping the schema to two entity types and two actions.

Integrate your IdP

The reference implementation uses Amazon Cognito with TOTP MFA, but most OIDC-compliant providers (Okta, Microsoft Entra ID, Auth0, or AWS IAM Identity Center) work with this pattern. The authentication-to-signing flow is described in the preceding Authentication before authorization section. To use a different IdP, replace the Cognito authorizer on API Gateway with a Lambda or JWT authorizer for your IdP’s issuer URL. Cedar policies remain unchanged.

Deploy the infrastructure with AWS CDK

The reference implementation deploys five CloudFormation stacks: KmsStack, VerifiedPermissionsStack, LambdaStack, SecurityLakeStack, and MonitoringStack. The following commands deploy the stacks in dependency order:

cdk deploy KmsStack -c account_id=YOUR_ACCOUNT_ID -c guardrail_id=YOUR_GUARDRAIL_ID
cdk deploy VerifiedPermissionsStack -c account_id=YOUR_ACCOUNT_ID -c guardrail_id=YOUR_GUARDRAIL_ID
cdk deploy LambdaStack -c account_id=YOUR_ACCOUNT_ID -c guardrail_id=YOUR_GUARDRAIL_ID
cdk deploy SecurityLakeStack -c account_id=YOUR_ACCOUNT_ID -c guardrail_id=YOUR_GUARDRAIL_ID
cdk deploy MonitoringStack -c account_id=YOUR_ACCOUNT_ID -c guardrail_id=YOUR_GUARDRAIL_ID

Test the solution

Three end-to-end scenarios validate the evaluation model across different user roles, MFA states, and delegation depths. To run the tests:

  1. Set the API endpoint from the deployment output:
export API_ENDPOINT=$(aws cloudformation describe-stacks --stack-name LambdaStack \
  --query "Stacks[0].Outputs[?OutputKey=='ApiEndpoint'].OutputValue" --output text)
  1. Run the end-to-end tests:
.venv/bin/python -m pytest tests/e2e/ -v -s

The end-to-end tests cover the three scenarios described in the following sections. Each test sends a request through the deployed API and validates the per-layer authorization decisions.

Scenario A: Layer 3 enforcement

A support-role user (no MFA) requests record deletion through orchestrator and data-bot.

Layer Decision Reason
L1: Agent-to-tool PERMIT data-bot has trust level 4, namespace data, and lifecycle production
L2: Agent-to-agent PERMIT orchestrator is authorized to delegate to data-bot, depth within limits
L3: Originating user DENY User role is support, not admin; MFA not verified
Overall DENY Denying layer: L3

Without Layer 3, this request would have been permitted based on agent capabilities alone, demonstrating why originating user authorization is essential.

Scenario B: Authorized admin request

An admin user with MFA requests the same operation through the same chain.

Layer Decision Reason
L1 PERMIT Agent attributes match
L2 PERMIT Delegation path authorized
L3 PERMIT Role is admin, MFA verified, depth is less than or equal to 2
Overall PERMIT All three layers permit

Scenario C: Delegation depth limit

An admin with MFA requests the same operation, but the delegation chain has six hops. This scenario tests the Layer 2 depth constraint independently of user authorization.

Layer Decision Reason
L1 PERMIT Agent attributes match
L2 DENY Depth of six exceeds the hard limit of five
Overall DENY Denying layer: L2 (L3 not evaluated – halt)

Even an authorized admin can’t bypass the delegation depth constraint.

Alignment with the security principles for agentic AI

The AWS Office of the CISO published Four security principles for agentic AI systems. The following table shows how this solution maps to each principle.

Principle How the solution implements it
Secure development lifecycle across components Property-based testing (Hypothesis) for adversarial input fuzzing, Cedar policy formal verification with strict schema validation, end-to-end scenarios testing policy bypass and privilege escalation paths, and infrastructure-as-code (IaC) with AWS CDK.
Traditional security controls remain applicable AWS WAF, Amazon VPC isolation, AWS Key Management Service (AWS KMS) encryption, Amazon Cognito MFA, and Secrets Manager;
NIST SP 800-53 control mapping.
Deterministic external controls (security box) Three-layer Cedar evaluation runs outside the agent’s reasoning loop in a separate Lambda function.
HMAC-signed context prevents tampering.
Verified Permissions (the managed Cedar evaluation service) enforces L2 and L3 at the infrastructure level.
Greater autonomy earned through evaluation trust_level and lifecycle_stage policy attributes calibrate agent capabilities; OCSF 99001 audit events and Amazon CloudWatch dashboards provide the evidence base for expanding autonomy.

Monitoring and audit compliance

Each evaluation produces an OCSF 99001 audit event with request ID, user identity, delegation chain, per-layer decisions, and latency.

The following table maps this implementation to NIST SP 800-53 Rev. 5 controls. Customers are responsible for evaluating whether it meets their compliance requirements.

NIST control Control name How the reference implementation addresses it
AC-4 Information Flow Enforcement User context flows immutably through HMAC-signed envelopes
AC-6 Least Privilege Three-layer evaluation requires both agent capability and user role
AC-6(1) Authorize Access to Security Functions MFA required for high-risk tools in Layer 3
AC-6(5) Privileged Accounts Destructive operations restricted to admin with MFA verified
AU-2 Event Logging Each evaluation is logged as OCSF 99001
AU-3 Content of Audit Records Events include identity, chain, action, resource, decisions, and latency
SI-10 Information Input Validation HMAC verified before evaluation; Amazon Bedrock Guardrails on inbound
IA-2(1) Multi-factor Authentication Layer 3 enforces MFA for high-risk operations
SC-12 Cryptographic Key Management Signing key in Secrets Manager with rotation
SC-28 Protection of Information at Rest Policies in Verified Permissions with STRICT validation

Scaling to multi-account environments

Deploy the Cedar policy store in a central security account and use cross-account IAM roles for workload accounts to call verifiedpermissions:IsAuthorized. Use AWS Organizations service control policies (SCPs) to prevent workload accounts from creating their own policy stores. For standardizing user identity attributes across the organization, consider IAM Identity Center or a centralized OIDC provider that issues consistent claims to your workload accounts. This helps ensure that the context.originating_user attributes are uniform across accounts and agents.

For production deployments, consider extending this pattern with human-in-the-loop escalation for borderline denials, multi-tenant Cedar policy isolation, and Amazon Simple Storage Service (Amazon S3)-backed dynamic policy hot-reload for emergency tool shutdowns.

Clean up

To avoid ongoing charges, delete the deployed resources:

cdk destroy MonitoringStack SecurityLakeStack
cdk destroy LambdaStack
cdk destroy VerifiedPermissionsStack
cdk destroy KmsStack
aws logs delete-log-group --log-group-name /cedar-evaluator/audit  # if RETAIN policy

Conclusion

Multi-agent AI systems need authorization boundaries at every delegation hop. The three-layer Cedar policy model with OAuth 2.0 authentication provides that protection while maintaining least-privilege access. Combining a trusted IdP (AuthN) with Cedar policy evaluation (AuthZ) creates an authorization boundary around each tool invocation, verifying agent capability (L1), delegation path (L2), and originating user authority (L3). The pattern works with an OIDC-compliant IdP and a compute platform that can call Amazon Verified Permissions. Clone the reference implementation and adapt the Cedar policies to your organization’s requirements. For more information, see the Cedar policy language documentation and the Amazon Verified Permissions User Guide.

References

If you have feedback about this post, submit comments in the Comments section below.


Dhananjay Karanjkar

Dhananjay Karanjkar

Dhananjay is a Senior Lead Consultant at AWS Professional Services, specializing in agentic AI systems, multi-agent orchestration, and generative AI security. He holds two US patents and serves as a Responsible AI Champion, with a background spanning financial services, enterprise consulting, and enterprise-scale AI delivery. When not architecting AI solutions, he trains for triathlons, paints oil portraits, and is an avid reader.

Can I do that with policy? Understanding the AWS Service Authorization Reference

27 April 2026 at 18:01

Understanding what AWS Identity and Access Management (IAM) policies can control helps you build better security controls and avoid spending time on approaches that won’t work. You’ve likely encountered questions like:

  • Can I use AWS Organizations service control policies (SCPs) to prevent the creation of security groups that allow traffic from 0.0.0.0/0?
  • Can I block uploads unless objects are encrypted?
  • Can I prevent functions with more than 512 MB of memory allocated?

Some of these are possible with IAM policies. Others are not. The difference is determined by a fundamental principle of AWS authorization: Policies make decisions based on information available in the authorization context at the time of the API call.

In this blog post, you learn how to use the AWS Service Authorization Reference to determine what’s achievable with IAM policies, recognize scenarios that need alternative solutions, and build more effective security controls in your AWS environment.

Understanding AWS authorization context

When you make an AWS API request through the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDK, the specific AWS service (such as Amazon S3 or Amazon EC2) receiving the request assembles a request context containing information about that request. This context is used for policy evaluation decisions. Request context is structured using the Principal, Action, Resource, Condition (PARC) model, which has four key components.

  • Principal: Identifies the requester and their attributes (tags, session context)
  • Action: Specifies the AWS API operation being requested (for example, s3:PutObject, ec2:RunInstances)
  • Resource: Defines the target AWS resource using Amazon Resource Names (ARNs)
  • Condition: Provides additional context available at request time, such as IP address, time, encryption parameters, MFA status, and service-specific attributes

The following example shows the typical request context for an Amazon S3 object upload:

  • Principal: AIDA123456789EXAMPLE
  • Action: s3:PutObject
  • Resource: arn:aws:s3:::my-bucket/documents/samplereport.pdf
  • Condition:
    • aws:PrincipalTag/Department=Finance
    • aws:RequestedRegion=us-east-1
    • aws:SourceIp=x.x.x.x
    • aws:MultiFactorAuthPresent=true
    • s3:x-amz-server-side-encryption=AES256
    • s3:x-amz-storage-class=STANDARD_IA

IAM policies can evaluate request metadata like encryption method and storage class being specified. However, it cannot evaluate the actual file contents, object size, or specific data patterns. Policy evaluation occurs at the time of the request, using the information present in the authorization context.

An essential resource: The Service Authorization Reference

The Service Authorization Reference is the authoritative documentation for understanding what policies can control. For every AWS service, it documents:

  • Actions: Every controllable operation
  • Resources: Resource types that can be targeted
  • Condition keys: The exact context information available for policy decisions

Condition keys are broadly divided into two categories. Global condition keys, which can be used across AWS services, and service-specific condition keys, which are defined for use with an individual AWS service. Use the Service Authorization Reference to find the global-condition keys or service-specific condition keys for each AWS service.

How to use the Service Authorization Reference

Follow these steps to determine if your requirement can be controlled with IAM policies:

  1. Navigate to your service: Go to the page for the specific AWS service you’re working with, such as Actions, resources, and condition keys for Amazon S3.
  2. Find the action you want: Find the API operation you want to control. Be precise, different actions have different available condition keys.
  3. Examine available condition keys: The Condition keys column shows what context information AWS makes available for that action.
  4. Make your feasibility determination: If the information you need isn’t listed as a condition key, you will not be able to control it with IAM policies alone.

Let’s take an example from the Amazon Elastic Compute Cloud (Amazon EC2) ec2:RunInstances action to see what you can and can’t control. In the Service Authorization Reference under the Amazon EC2 section, examine the RunInstances action and check the Resource types column. The RunInstances action affects multiple resource types, each with its own set of condition keys.

For the instance* resource type:

  • ec2:InstanceType: Can restrict instance types
  • ec2:EbsOptimized: Can require EBS optimization
  • aws:RequestTag/: Can enforce tagging requirements

For the network-interface* resource type:

  • ec2:Subnet: Can control subnet placement
  • ec2:Vpc: Can limit to specific virtual private clouds (VPCs)
  • ec2:AssociatePublicIpAddress: Can control public IP assignment

Note: These are a few examples from the many condition keys available for each resource type under the RunInstances action. The Service Authorization Reference lists dozens of condition keys across resource types (instance, network interface, security group, subnet, volume, and so on) that RunInstances affects. Consult the complete reference to see the available options for your specific use case.

Access the Service Authorization Reference programmatically

Beyond the human-readable documentation, AWS provides the Service Authorization Reference in machine-readable JSON format to streamline automation of policy management workflows. Use this programmatic access to incorporate authorization metadata into your development and security workflows.
For detailed information about the JSON structure and field definitions, see the Simplified AWS service information for programmatic access.
Developers can use tools like the IAM MCP Server for AWS IAM operations. This server provides AI assistants with the ability to manage IAM users, roles, policies, and permissions while following security best practices.

Using IAM policies to control specific scenarios

The following examples show how you can use IAM policies to control specific scenarios.

Example 1: Enforce AES256 server-side encryption on S3 objects

In the Amazon S3 Service Authorization Reference, under s3:PutObject action, the s3:x-amz-server-side-encryption condition key is available in the authorization context, which can be used to control the server-side encryption of S3 objects with AES-256. Here is the required policy.

Policy 1: Deny Amazon S3 object upload if the encryption doesn’t use AES-256

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Sid": "DenyUnencryptedObjectUploads",
			"Effect": "Deny",
			"Action": "s3:PutObject",
			"Resource": "arn:aws:s3:::my-bucket/*",
			"Condition": {
				"StringNotEquals": {
					"s3:x-amz-server-side-encryption": "AES256"
				}
			}
		}
	]
}

Policy 1 is a resource-based policy that can be applied on an S3 bucket to restrict object uploads. It denies a PutObject request when the server-side encryption isn’t using the AES-256 encryption algorithm.

Example 2: Allow different instance types based on the user’s cost center tag.

When checking the Amazon EC2 Service Authorization Reference for ec2:RunInstances, the ec2:InstanceType condition key, which is resource specific, is available. To restrict instance types based on who is launching them (rather than just what is being launched), you can either combine this with a global condition key or attach different policies to different principals. By using aws:PrincipalTag/tag-key alongside ec2:InstanceType, you can identify the user’s cost center from their IAM identity tags and then apply different instance type restrictions accordingly. This allows a single policy to dynamically enforce different permissions based on the requester’s identity.

Policy 2: Restricting EC2 instance types by cost center

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Sid": "AllowDevInstanceTypes",
			"Effect": "Allow",
			"Action": "ec2:RunInstances",
			"Resource": "arn:aws:ec2:*:*:instance/*",
			"Condition": {
				"StringEquals": {
					"aws:PrincipalTag/CostCenter": "Development"
				},
				"StringLike": {
					"ec2:InstanceType": "t3.*"
				}
			}
		},
		{
			"Sid": "AllowProdInstanceTypes",
			"Effect": "Allow",
			"Action": "ec2:RunInstances",
			"Resource": "arn:aws:ec2:*:*:instance/*",
			"Condition": {
				"StringEquals": {
					"aws:PrincipalTag/CostCenter": "Production"
				},
				"StringLike": {
					"ec2:InstanceType": [
						"m5.*",
						"c5.*",
						"r5.*"
					]
				}
			}
		}
	]
}

This is an identity-based policy that you can attach to IAM users, groups, or roles to control EC2 instance launches based on cost allocation. In the first statement, aws:PrincipalTag, which is a global condition key (tags attached to the IAM user or role), is used to determine which instance types are allowed. Users tagged with CostCenter=Development can only launch cost-effective T3 instance types (t3.micro, t3.small, t3.medium, and so on)with the service specific key ec2:InstanceType.

In the second statement, users tagged with CostCenter=Production can launch more powerful instance types from the M5 (general purpose), C5 (compute optimized), and R5 (memory optimized) families. This approach lets organizations enforce cost controls and allocate resources based on workload requirements. Each cost center maintains flexibility for its specific needs.

Note: Additional resources are required in the IAM policy to successfully launch EC2 instances. For the complete list, see Launch Instances.

Example 3: Users can only access and update DynamoDB items where the partition key matches their username.

You have identified that GetItem, PutItem,and UpdateItem actions are required. Corresponding to these actions, you can use the condition key to expose partition key values in the authorization context as described in the Amazon DynamoDB Service Authorization Reference

Policy 3: DynamoDB fine-grained access control

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Effect": "Allow",
			"Action": [
				"dynamodb:GetItem",
				"dynamodb:PutItem",
				"dynamodb:UpdateItem"
			],
			"Resource": "arn:aws:dynamodb:us-east-1:111122223333:table/UserProfiles",
			"Condition": {
				"ForAllValues:StringEquals": {
					"dynamodb:LeadingKeys": ["${aws:username}"]
				}
			}
		}
	]
}

The policy allows users to perform read and write actions (GetItem, PutItem, and UpdateItem) on the UserProfiles table, but only for items where the partition key value equals their own username (using the ${aws:username} policy variable). For example, if user alice attempts to access an item with partition key bob, the request will be denied.

Scenarios that need more than policies alone

Some requirements can’t be met using IAM policies. Here are three common scenarios that aren’t achievable with IAM policies alone.

Scenario 1: Block users from creating security group rules that allow traffic from 0.0.0.0/0 on TCP port 22

Upon checking the Amazon EC2 Service Authorization Reference, you will find that the ec2:AuthorizeSecurityGroupIngress action is required in an IAM policy to add an inbound access rules to a security group.

To verify this in the Service Authorization Reference, navigate to the Amazon EC2 Service Authorization Reference and search for the AuthorizeSecurityGroupIngress action, which is the action that creates security group rules. After you locate this action, review the Condition keys column and look for condition keys related to CIDR blocks, IP ranges, ports, or protocols. Available condition keys for ec2:AuthorizeSecurityGroupIngress include:

Notice there are no condition keys for CIDR blocks (such as 0.0.0.0/0), port numbers (such as 22), or protocols (such as TCP). The authorization context doesn’t include information about the specific CIDR blocks, ports, or protocols being added to the security group rule, so IAM policies can’t control these attributes.

Solution
Take a reactive approach using the AWS Config managed rule INCOMING_SSH_DISABLED to detect overly permissive rules. You can also use a combination of Amazon EventBridge and Lambda to either send a notification to your security team for the non-compliant configuration or to restrict the security group through an automation. For more information, see How to Automatically Revert and Receive Notifications About Changes to Your Amazon VPC Security Groups.

Scenario 2: Prevent creation of Lambda functions with more than 512 MB of memory allocated

Following the same verification methodology described in Scenario 1, navigate to the AWS Lambda Service Authorization Reference and examine the CreateFunction action’s condition keys for the function* resource type.

Available condition keys for lambda:CreateFunction with the function* resource type include:

  • lambda:CodeSigningConfigArn: Filters access by the ARN of the code signing
  • configuration-lambda:Layer: Filters access by the ARN of a version of an AWS Lambda layer
  • lambda:VpcIds: Filters access by the ID of the VPC configured for the Lambda function

There is no condition key for memory allocation (MemorySize parameter), timeout settings, storage configuration (EphemeralStorage), or runtime selection. Because memory allocation isn’t exposed in the authorization context, IAM policies can’t restrict this parameter.

Solution

Key takeaways

Keep these principles in mind when working with IAM policies:

  • Policies control what’s in the authorization context, not all elements you see in API documentation
  • The Service Authorization Reference is authoritative; if something isn’t listed as a condition key, you can’t control it with policies
  • Different actions have different available contexts even within the same service
  • Alternative approaches exist. AWS Config, EventBridge, and service-specific controls can be used to achieve your goals when policies alone can’t
  • Layered security is essential; combine preventive, detective, and responsive controls to help ensure that your data is secure

Conclusion

In this post, you learned how to use the AWS Service Authorization Reference to determine what’s achievable with IAM policies and recognize scenarios that require alternative solutions. By understanding that policies can only make decisions based on information available in the authorization context, you can build more effective security controls and avoid spending time on approaches that won’t work.

The Service Authorization Reference is your authoritative source for understanding policy capabilities. When you need to implement a control, start there to see if the required condition keys exist. If they don’t, you will need to layer in detective or responsive controls using services like AWS Config, Amazon EventBridge, or AWS Lambda.

Remember that effective AWS security isn’t about finding one perfect control, it’s about combining preventive, detective, and responsive measures to create defense in depth. IAM policies are powerful tools for prevention and work as part of a comprehensive security strategy.

Next steps:

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Author

Anshu Bathla

Anshu is a Senior Lead Consultant – SRC at AWS, based in Gurugram, India. He works with customers across diverse verticals to help strengthen their security infrastructure and achieve their security goals. Outside of work, Anshu enjoys reading books and gardening at his home garden.

Author

Prafful Gupta

Prafful is an Associate Delivery Consultant at AWS, based in Gurugram, India. Having started his professional journey with Amazon, he specializes in DevOps and Generative AI solutions, helping customers navigate their cloud transformation journeys. Beyond work, he enjoys networking with fellow professionals and spending quality time with family.

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