Amazon Q & IAM Access Analyzer: Catch Dangerous IAM Policies Early

Learn how to maintain both development velocity and security best practices by combining Amazon Q's rapid IAM policy generation with AWS IAM Access Analyzer's deterministic validation. This post demonstrates a practical workflow for catching security issues in Q-generated policies before deployment, including overly permissive wildcards and privilege escalation risks, with automated remediation and CI/CD integration for your serverless applications.

Cassia Melvin

GenAI Specialist & Frontend Developer (React, Next.js)

Published Aug 1, 2025

Last modified Aug 1, 2025

The Challenge: Balancing Speed and Security in Infrastructure as Code

When building serverless applications with AWS CDK, it's tempting to focus on getting your infrastructure deployed quickly. However, this often leads to overly permissive IAM policies that violate the principle of least privilege. In this post, I'll show you how to use Amazon Q to generate IAM policies and infrastructure code rapidly, then validate them using AWS IAM Access Analyzer to maintain a strong security posture.

The Problem: Generated Policies Need Validation

Consider this common scenario: you're building a task management system with role-based access control. Amazon Q helps you generate the infrastructure code quickly, but how do you ensure the generated IAM policies follow security best practices?

Let's look at a problematic policy that might be generated:

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{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "*",
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": "s3:GetObject",
            "Resource": "arn:aws:s3:::my-bucket/*",
            "Principal": "*"
        }
    ]
}

This policy has several security issues:

Wildcard permissions ("Action": "*")
Overly broad resource access ("Resource": "*")
Public access to S3 resources ("Principal": "*")

The Solution: Automated Policy Validation

I've created a Python-based IAM policy validator that integrates AWS IAM Access Analyzer into your development workflow. This tool helps catch security issues before deployment.

Key Features

GUI Interface: Easy-to-use graphical interface for policy validation
AWS Integration: Leverages IAM Access Analyzer API for comprehensive policy analysis
Policy Type Support: Validates both identity and resource-based policies
Detailed Findings: Provides specific recommendations for policy improvements
CI/CD Ready: Can be integrated into automated deployment pipelines

Implementation

Here's the core validator implementation:

IAM Policy Validator using AWS Access Analyzer

Real-World Example: Task Manager Application

In my task management system built with AWS CDK, I use this validator to ensure the Lambda execution roles follow least privilege principles. Here's how the CDK-generated policies are validated:

Validation Results

When I run the validator on the problematic policy shown earlier, it identifies several critical issues:

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📋 Validating Policy: problematic-policy.json
==================================================
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "*",
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::my-bucket/*",
      "Principal": "*"
    }
  ]
}

⚠️  VALIDATION FINDINGS (3 issues)

⚠️ Finding #1: WARNING
   Issue: CREATE_SLR_WITH_STAR_IN_ACTION_AND_RESOURCE
   Details: Using wildcards (*) in the action and the resource can allow creation of unintended service-linked roles because it allows iam:CreateServiceLinkedRole permissions on all resources. We recommend that you specify resource ARNs instead.

⚠️ Finding #2: SECURITY_WARNING
   Issue: PASS_ROLE_WITH_STAR_IN_ACTION_AND_RESOURCE
   Details: Using wildcards (*) in the action and the resource can be overly permissive because it allows iam:PassRole permissions on all resources. We recommend that you specify resource ARNs or add the iam:PassedToService condition key to your statement.

🚨 Finding #3: ERROR
   Issue: UNSUPPORTED_PRINCIPAL
   Details: The policy type IDENTITY_POLICY does not support the Principal element. Remove the Principal element.

Integration with CI/CD Pipeline

To enforce IAM best practices in your deployment pipeline, integrate the validator as a pre-deployment check:

Amazon Q Integration Workflow

Here's the complete workflow showing Amazon Q generation, Access Analyzer validation, and automatic remediation:

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🤖 Amazon Q + IAM Access Analyzer Demo
==================================================

1️⃣ Amazon Q Generated Policy (Initial):
Prompt: 'Create IAM policy for Lambda accessing DynamoDB and S3'
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "*",
      "Resource": "*"
    }
  ]
}

2️⃣ Access Analyzer Validation:
⚠️  Found 2 security issues:
   ⚠️ Issue #1: CREATE_SLR_WITH_STAR_IN_ACTION_AND_RESOURCE
      Details: Using wildcards (*) in the action and the resource can allow creation of unintended service-linked roles
   ⚠️ Issue #2: PASS_ROLE_WITH_STAR_IN_ACTION_AND_RESOURCE
      Details: Using wildcards (*) in the action and the resource can be overly permissive

3️⃣ Amazon Q Remediation Prompt:
Fix this IAM policy to address security issues:
- CREATE_SLR_WITH_STAR_IN_ACTION_AND_RESOURCE
- PASS_ROLE_WITH_STAR_IN_ACTION_AND_RESOURCE

4️⃣ Amazon Q Remediated Policy:
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "dynamodb:GetItem",
        "dynamodb:PutItem",
        "dynamodb:UpdateItem",
        "dynamodb:DeleteItem",
        "dynamodb:Query"
      ],
      "Resource": [
        "arn:aws:dynamodb:us-east-1:*:table/TasksTable",
        "arn:aws:dynamodb:us-east-1:*:table/UserProfilesTable"
      ]
    },
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:PutObject"
      ],
      "Resource": "arn:aws:s3:::task-attachments/*"
    }
  ]
}

5️⃣ Validating Remediated Policy:
✅ Remediated policy passes all security checks!
🎉 Ready for deployment

Benefits of This Approach

1. Automated Security Review

Catches common IAM misconfigurations before deployment
Provides specific remediation guidance
Reduces manual security review overhead

2. Developer-Friendly

GUI interface for easy policy testing
Integrates with existing development workflows
Provides clear, actionable feedback

3. Compliance Assurance

Enforces least privilege principles
Document security validation process
Supports audit requirements

4. CI/CD Integration

Blocks deployments with security issues
Provides automated policy validation
Maintains security standards across teams

🧪 Try It Yourself: Set Up the Validator Locally

To run the Python GUI IAM analyzer application:

Clone the IAM Policy Validator to generate IAM policies and infrastructure code rapidly project on GitHub. The project also contains a sample 'Task Manager' CDK application with RBAC resources to provide a realistic example built by Q Developer.
Browse to the infrastructure GitHub folder and follow the README.
Install Dependencies:

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python3 -m venv .venv
source .venv/bin/activate
pip install boto3

Configure AWS Credentials:

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export AWS_ACCESS_KEY_ID=YOUR_ACCESS_KEY_ID
export AWS_SECRET_ACCESS_KEY=YOUR_SECRET_ACCESS_KEY
export AWS_DEFAULT_REGION=YOUR_REGION
or
aws sso login --profile your-profile

Run the Validator:

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python3 iam_policy_validator.pypy

Integrate with Your Pipeline:

Add a validation step to CI/CD
Configure policy validation gates
Set up automated remediation workflows

infrastructure

Conclusion

By combining Amazon Q's rapid code generation capabilities with AWS IAM Access Analyzer's policy validation, you can maintain both development velocity and security best practices. This approach ensures that your infrastructure code is not only functional but also secure by design.

The key is to make security validation a seamless part of your development workflow, not an afterthought. With automated policy validation, you can catch security issues early and maintain a strong security posture throughout your application lifecycle.

💡 Try It Yourself!
Want to secure your own CDK apps?
👉 Check out the source code here: GitHub – IAM Policy Validator

📫 I’d love your feedback or suggestions—connect with me on LinkedIn or follow me on GitHub !

Resources

# iam # devops # security # amazon-q-developer # aws-cdk

Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.

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Amazon Q & IAM Access Analyzer: Catch Dangerous IAM Policies Early

Cassia Melvin

The Challenge: Balancing Speed and Security in Infrastructure as Code

The Problem: Generated Policies Need Validation

The Solution: Automated Policy Validation

Key Features

Implementation

Real-World Example: Task Manager Application

Validation Results

Integration with CI/CD Pipeline

Amazon Q Integration Workflow

Benefits of This Approach

1. Automated Security Review

2. Developer-Friendly

3. Compliance Assurance

4. CI/CD Integration

🧪 Try It Yourself: Set Up the Validator Locally

Conclusion

Resources