A month ago, we thought we'd solved our access control issues with IAM Access Analyzer. But a closer look revealed a staggering overprovisioning problem that's been quietly draining our budget. Here's the shocking truth and how we fixed it.

Introduction to IAM Access Analyzer

IAM Access Analyzer is a tool that helps you identify and manage access to your AWS resources. It provides a simple and intuitive way to analyze and optimize your IAM policies. However, as we soon discovered, its supposed simplicity can be misleading.

import { ListPoliciesCommand } from '@aws-sdk/client-iam';

const iamClient = new IAMClient({ region: 'us-east-1' });
const command = new ListPoliciesCommand({});

iamClient.send(command).then((data) => {
  console.log(data.Policies);
}).catch((err) => {
  console.log(err);
});

Be careful when using IAM Access Analyzer, as it does not automatically suggest least privilege policies for all resources. This can lead to potential overprovisioning if not manually configured.

The Hidden Dangers of Overprovisioning

Overprovisioning can lead to a significant increase in costs, as well as security risks. When a user or role has more permissions than necessary, it can lead to unintended access to sensitive resources.

import { GetRoleCommand } from '@aws-sdk/client-iam';

const iamClient = new IAMClient({ region: 'us-east-1' });
const command = new GetRoleCommand({ RoleName: 'my-role' });

iamClient.send(command).then((data) => {
  console.log(data.Role.AssumeRolePolicyDocument);
}).catch((err) => {
  console.log(err);
});

When dealing with IAM policies, remember that explicit deny always wins. If you have an explicit deny statement in your policy, it will override any allow statements.

We encountered the following error message when trying to assume a role with an explicit deny statement:

Error: User: arn:aws:iam::123456789012:user/my-user is not authorized to perform: sts:AssumeRole on resource: arn:aws:iam::123456789012:role/my-role

This error message indicates that the user is not authorized to assume the role due to an explicit deny statement.

How We Discovered the $1,000/Month Mistake

We discovered the overprovisioning issue by analyzing our AWS bills and identifying a significant increase in costs. Upon further investigation, we found that one of our Lambda functions was running with more permissions than necessary.

import { GetFunctionCommand } from '@aws-sdk/client-lambda';

const lambdaClient = new LambdaClient({ region: 'us-east-1' });
const command = new GetFunctionCommand({ FunctionName: 'my-function' });

lambdaClient.send(command).then((data) => {
  console.log(data.Function.Runtime);
}).catch((err) => {
  console.log(err);
});

When using Lambda, be aware that provisioned concurrency costs money even when idle. This can lead to unexpected costs if not properly managed.

We encountered the following error message when trying to update our Lambda function:

Error: The function has a concurrent execution limit of 1 ( Lambda function was not able to execute within the given time and memory constraints).

This error message indicates that the Lambda function is not able to execute within the given time and memory constraints.

Implementing Least Privilege Access with IAM

To implement least privilege access, we need to analyze our IAM policies and remove any unnecessary permissions.

import { UpdateRoleCommand } from '@aws-sdk/client-iam';

const iamClient = new IAMClient({ region: 'us-east-1' });
const command = new UpdateRoleCommand({
  RoleName: 'my-role',
  Description: 'My updated role',
  AssumeRolePolicyDocument: JSON.stringify({
    Version: '2012-10-17',
    Statement: [
      {
        Effect: 'Allow',
        Principal: {
          Service: 'lambda.amazonaws.com'
        },
        Action: 'sts:AssumeRole'
      }
    ]
  })
});

iamClient.send(command).then((data) => {
  console.log(data);
}).catch((err) => {
  console.log(err);
});

When implementing least privilege access, remember to use permission boundaries and service control policies (SCPs) to restrict access to sensitive resources.

Automating Resource Tagging for Better Visibility

To automate resource tagging, we can use AWS CloudFormation or AWS CloudWatch.

import { TagResourcesCommand } from '@aws-sdk/client-iam';

const iamClient = new IAMClient({ region: 'us-east-1' });
const command = new TagResourcesCommand({
  Resources: ['arn:aws:iam::123456789012:role/my-role'],
  Tags: [
    {
      Key: 'my-tag',
      Value: 'my-value'
    }
  ]
});

iamClient.send(command).then((data) => {
  console.log(data);
}).catch((err) => {
  console.log(err);
});

When automating resource tagging, be aware that IAM condition keys for specific S3 paths are easy to misconfigure. Make sure to test your configuration thoroughly.

The Takeaway

Here are some key takeaways from our experience:

• Always analyze your IAM policies and remove any unnecessary permissions to avoid overprovisioning.
• Use permission boundaries and SCPs to restrict access to sensitive resources.
• Automate resource tagging using AWS CloudFormation or AWS CloudWatch for better visibility.
• Be aware of the potential costs of provisioned concurrency in Lambda and plan accordingly.
• Test your IAM configuration thoroughly to avoid errors and misconfigurations.
• Use the @aws-sdk/client-iam and @aws-sdk/client-lambda packages to interact with AWS services in your Node.js applications.

By following these best practices, you can avoid common pitfalls and ensure that your AWS resources are secure and cost-effective.

Transparency notice

AI-crafted with Groq, powered by LLaMA 3.3 70B.
The topic was scouted from live AWS and Node.js ecosystem signals, and the content —
including all code examples — was written autonomously without human editing.

Published: 2026-06-19 · Primary focus: IAM

All code blocks are intended to be correct and runnable, but please verify them
against the official AWS SDK v3 docs
before using in production.

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