IAM Policy Autopilot

An open source Model Context Protocol (MCP) server and command-line tool that helps your AI coding assistants quickly create baseline IAM policies that you can refine as your application evolves, so you can build faster. IAM Policy Autopilot analyzes your application code locally to generate identity-based policies for application roles, enabling faster IAM policy creation and reducing access troubleshooting time. IAM Policy Autopilot supports policy generation for applications built in Python, Go, TypeScript, JavaScript, and Java — see Supported Languages and SDKs for policy generation.

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Table of Contents

• Who is IAM Policy Autopilot for?
• How is IAM Policy Autopilot helpful?
• Best Practices and Considerations
• Getting Started
• Network Requirements
• CLI Usage
• Supported Languages and SDKs for policy generation
• Build Instructions
• Workspace Structure
• Development
• Security
• License

Who is IAM Policy Autopilot for?

IAM Policy Autopilot is for builders on AWS using AI coding assistants, including developers, product managers, technical experimenters, and business leaders.

How is IAM Policy Autopilot helpful?

IAM Policy Autopilot is:

Fast

IAM Policy Autopilot accelerates development by generating baseline identity-based IAM policies. Your AI coding assistant can call IAM Policy Autopilot to analyze AWS SDK calls within your application. IAM Policy Autopilot then automatically creates the baseline IAM permissions for your application roles.

Reliable

IAM Policy Autopilot's deterministic code analysis helps create reliable and valid IAM policies that reduce policy troubleshooting. By using valid policies created with the MCP server, you reduce time spent on policy-related debugging and accelerate application deployment by avoiding permission-related delays.

Up-to-date

IAM Policy Autopilot stays up to date with the latest AWS services and features so that builders and coding assistants have access to the latest AWS IAM permissions knowledge. It helps keep your application role's permissions current with AWS's evolving capabilities.

Best Practices and Considerations

Review and refine policies generated by IAM Policy Autopilot

IAM Policy Autopilot generates baseline policies to provide a starting point that you can refine as your application matures. Review the generated policies to ensure they align with your security requirements before deploying them. Use the --explain feature with action patterns (e.g., --explain 's3:*') to understand which operations led to an action being included in the generated policies.

Understand the IAM Policy Autopilot scope

IAM Policy Autopilot produces IAM identity-based policies, but doesn't support resource-based policies such as S3 bucket policies or KMS key policies, Resource Control Policies (RCPs), Service Control Policies (SCPs), and permission boundaries. These are the limitations that you need to keep in mind. For example, if your code calls s3.getObject(bucketName) where bucketName is determined at runtime, IAM Policy Autopilot currently doesn't predict which bucket will be accessed.

Understand the boundary between IAM Policy Autopilot and your coding assistant

IAM Policy Autopilot generates policies with specific actions based on deterministic analysis of your code. When you use the MCP server integration, your AI coding assistant receives this policy and might modify it when creating infrastructure-as-code templates. For example, you might see the assistant add specific resource Amazon Resource Names (ARNs) or include KMS key IDs based on additional context from your code. These changes come from your coding assistant's interpretation of your broader code context, not from the static analysis provided by IAM Policy Autopilot. Always review content generated by your coding assistant before deployment to verify that it meets your security requirements.

Use service hints for accurate policies

IAM Policy Autopilot's static analysis may include permissions for AWS services your application doesn't use. This happens when method names in your code match AWS SDK calls from multiple services. For example, a method called listAccounts() might generate permissions for both AWS Organizations and Amazon Chime services.

Recommended approach: Use the --service-hints option to specify only the AWS services your application actually uses. This helps IAM Policy Autopilot scope down which SDK calls to analyze, but the final policy may still include actions from other services if they're required by the operations you perform:

# More accurate - specify only services you use
iam-policy-autopilot generate-policies ./src/app.py --service-hints s3 iam organizations --pretty

# Less accurate - may include unnecessary permissions
iam-policy-autopilot generate-policies ./src/app.py --pretty

This significantly reduces unnecessary permissions and generates more targeted policies. Note that the final policy may still include actions from services not in your hints if they're required for the operations you perform (e.g., KMS actions for S3 encryption).

Note: When using the MCP server integration with AI coding assistants, the assistant is expected to automatically provide appropriate service hints based on your code context. The --service-hints option is primarily for CLI usage.

Supported Languages and SDKs for policy generation

Getting Started

Installation

Option 1: Using uv (Recommended)

Install uv from Astral.

No additional installation needed - you can run IAM Policy Autopilot directly using uvx iam-policy-autopilot.

Option 2: Using pip

Install pip.

pip install iam-policy-autopilot

Option 3: Direct installation (MacOS/Linux only)

To install the latest release directly, run the following script to download and install as a system utility.

curl -sSL https://github.com/awslabs/iam-policy-autopilot/raw/refs/heads/main/install.sh | sudo sh

This will install the latest release directly to /usr/local/bin/iam-policy-autopilot.

AWS Configuration

IAM Policy Autopilot requires AWS credentials to apply policy fixes and upload policies for AccessDenied debugging.

Install AWS CLI and configure your AWS credentials.

For more information on AWS credential configuration, see the AWS CLI Configuration Guide.

MCP Server Configuration

Configure the MCP server in your MCP client configuration to enable your AI coding assistant to generate IAM policies.

For Kiro

Get Kiro from https://kiro.dev/

If using uv/uvx:

Add the following configuration to your project-level .kiro/settings/mcp.json:

{
  "mcpServers": {
    "iam-policy-autopilot": {
      "command": "uvx",
      "args": ["iam-policy-autopilot", "mcp-server"],
      "env": {
        "AWS_PROFILE": "your-profile-name",
        "AWS_REGION": "us-east-1"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

If using pip:

{
  "mcpServers": {
    "iam-policy-autopilot": {
      "command": "iam-policy-autopilot",
      "args": ["mcp-server"],
      "env": {
        "AWS_PROFILE": "your-profile-name",
        "AWS_REGION": "us-east-1"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

For Kiro CLI

Get Kiro CLI from https://kiro.dev/cli

Kiro Cli uses the same configuration as Kiro mentioned above, additionally, MCPs for Kiro CLI can also be setup via:

If using uv/uvx:

kiro-cli mcp add \
  --name iam-policy-autopilot \
  --command "uvx" \
  --args "iam-policy-autopilot","mcp-server"

If using pip:

`