AWS, like most security-minded organizations, recommends following the security practice of least privilege. This practice is fundamental to reducing the attack surface and ensuring that users, roles, and systems only have the minimum necessary permissions to perform their required tasks. For many use cases, least privilege can be a relatively simple task to implement. For example, if a handful of instances need to receive HTTPS traffic from an Elastic Load Balancer (ELB), or if a Relational Database Service (RDS) cluster should only accept requests from specific application servers, the implementation is straightforward. However, the introduction of automation and Infrastructure as Code (IaC) tools like Terraform can complicate the process.
The inclination often is to provide Terraform users with broad permissions to allow them to deploy and manage all resources within the AWS environment. This approach, while seemingly practical, can be dangerous and goes against the principle of least privilege. This article will explore strategies for applying least privileged access within the context of Terraform and AWS, focusing on reducing the blast radius of potential security incidents and applying the right permissions where they are needed.
Challenges with Broad Permissions in AWS
A common mistake many cloud engineers make is granting administrative privileges to IAM users or groups. One of the most frequently used permission policies is ‘AdministratorAccess’. This policy grants full access to every AWS service and resource, which might seem like an easy solution for Terraform users who need to create and modify resources across multiple AWS services. However, this practice is not only insecure but also inefficient.
When users are granted full administrative access, they can make any changes within the environment, including changes that may unintentionally break configurations, expose sensitive data, or introduce security vulnerabilities. The problem is exacerbated when users have programmatic keys that can be used to interact with AWS resources programmatically. Without careful management, these keys can be compromised, leading to severe security breaches. AWS does not rotate these keys automatically, leaving customers responsible for managing their security, as outlined in the Shared Responsibility Model. If keys are compromised and not rotated regularly, attackers can use them indefinitely until detected. This risk highlights the importance of minimizing access and following best practices around key management.
The Role of AWS Roles in Least Privilege Access
One of the most effective ways to limit permissions and reduce risk in AWS is by using IAM roles. Roles are AWS identities to which permissions can be attached, and they provide a more flexible and secure way of managing access compared to directly assigning permissions to IAM users. Unlike IAM users, roles are not tied to specific user accounts but can be assumed by trusted entities, including other users, services, or applications. When a user assumes a role, they temporarily gain the permissions assigned to that role for a defined duration, typically one hour by default.
For example, in a Terraform setup, an IAM user might not need full administrative permissions but may require the ability to create and manage specific resources. Instead of assigning broad administrative permissions, the user can assume a role that grants only the necessary permissions for a given task. The process of assuming a role is done through the AWS Security Token Service (STS), which generates temporary security credentials that can be used for making API calls.
In the AWS CLI, assuming a role would look like this:
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aws sts assume-role –role-arn arn:aws:iam::123:role/terraform_admin –role-session-name terraform_lab –external-id aws-is-cool
In this example, the user is assuming the “terraform_admin” role. The session name “terraform_lab” is a label for the session, and the external ID “aws-is-cool” is an additional layer of security that helps prevent unauthorized role assumption. Once the role is assumed, temporary credentials are generated, and the IAM user can interact with AWS resources as if they were assigned those permissions directly.
This process is crucial because even if the temporary credentials are exposed, they are only valid for a short period (one hour by default), which limits the potential damage if they fall into the wrong hands. This way, users don’t need long-lasting administrative permissions, and permissions are granted only when needed and for a limited time.
Implementing Least Privilege Access in Terraform
Terraform, as an Infrastructure as Code tool, typically uses programmatic access keys linked to IAM users to make API calls to AWS. By default, Terraform uses these keys to authenticate and authorize API requests. While this is functional, it is not the most secure approach. It would require manual updates to these credentials every hour if role assumption was implemented directly through the AWS CLI. Fortunately, Terraform provides the flexibility to integrate AWS role assumption directly within its configuration.
To achieve least privilege in Terraform, the first step is to authenticate using IAM credentials, which will allow the user to assume a role that has the necessary permissions for provisioning and managing infrastructure. This can be done through Terraform’s AWS provider configuration. In Terraform, roles can be defined for different purposes to limit access based on specific needs.
For instance, you could define two separate roles:
- terraform_state: This role would be used only for saving the Terraform state to an S3 bucket. Since the state file contains sensitive information, this role should be limited to only the permissions necessary to access and manage the S3 bucket and its contents.
- terraform_provisioning: This role would have permissions to create and manage specific AWS resources like EC2 instances, Lambda functions, or EKS clusters. These permissions would be granted on a resource-by-resource basis, ensuring that the user or system provisioning the resources only has the necessary access.
By creating and utilizing separate roles for different tasks, you can reduce the potential impact of a compromised set of credentials. The “terraform_state” role is confined to managing state storage, while the “terraform_provisioning” role is limited to infrastructure management tasks. This separation of duties ensures that no single set of credentials has too much power, further reducing the risk associated with using Infrastructure as Code.
This approach also makes the process more manageable. Rather than granting users full administrative privileges, you give them exactly the permissions they need, based on their role in the system. If the credentials for the “terraform_provisioning” role are compromised, the attacker would only have limited access to the resources defined in the role, not the entire AWS environment. This isolation of duties is a critical aspect of applying the least privilege principle in cloud environments.
In conclusion, by utilizing IAM roles and assuming those roles with temporary credentials, AWS and Terraform provide a solid foundation for implementing least privileged access in the cloud. This method ensures that users only have the permissions they need and for a limited time, significantly reducing the attack surface and minimizing the impact of any potential security incidents.
Automating Least Privilege with Terraform and AWS
Building on the foundation of least privileged access and role-based security, the next step is automating the process within Terraform and AWS. While roles provide a significant security benefit, the management of credentials and role assumptions can become cumbersome if not handled effectively. The goal of this section is to explore how automation, specifically in Terraform, can streamline the process of assigning temporary access and prevent the need for manual intervention.
Terraform enables the creation of infrastructure as code and automates the provisioning of resources. By automating role assumption and credential management, you can ensure that your cloud environment remains secure without requiring manual updates or constant monitoring. This integration can be achieved by configuring AWS provider settings in Terraform to automatically assume roles and use temporary credentials.
Using the AWS Provider to Assume Roles in Terraform
To automate the process of assuming roles with Terraform, you must configure the AWS provider to handle role assumption for you. This eliminates the need for manual intervention and ensures that permissions are granted in a controlled and automated manner.
In Terraform, you can use the assume_role configuration in the AWS provider to automatically assume a role when provisioning resources. This is done by specifying the ARN of the role and providing the necessary parameters to allow Terraform to assume the role, such as the external ID and session name. For example:
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provider “aws” {
region = “us-east-1”
access_key = “AKIA….”
secret_key = “….”
assume_role {
role_arn = “arn:aws:iam::123456789012:role/terraform_provisioning”
session_name = “terraform_session”
external_id = “aws-is-cool”
}
}
In this example, the AWS provider is configured to assume the role terraform_provisioning whenever Terraform applies changes. The provider uses the specified role_arn, which points to the correct IAM role in AWS, and the session name terraform_session, which identifies the session uniquely. The external_id is an additional layer of security, preventing unauthorized users from assuming the role.
This configuration ensures that Terraform uses temporary security credentials that are generated when assuming the role. These credentials are valid only for a short period (default is one hour), which limits the risk of long-term exposure in the event of a security breach.
By automating the assumption of roles in this manner, you ensure that users and systems interacting with AWS through Terraform never directly use long-lived credentials. This reduces the possibility of malicious access if a user’s credentials are exposed or misused.
Setting Up Temporary Credentials in Terraform
When Terraform assumes a role, it receives a set of temporary security credentials, which include an access key, a secret key, and a session token. These credentials are valid for a limited time (typically one hour) and are automatically rotated when the session expires. This ensures that even if credentials are exposed or leaked, their effectiveness is short-lived, providing a significant security benefit.
Terraform’s AWS provider automatically manages the temporary credentials, including their expiration and rotation. However, you must configure the provider to ensure that the temporary credentials are used correctly for API requests.
For example, once Terraform assumes the role, it will automatically use the temporary credentials for subsequent API calls to AWS. These credentials are stored in the environment variables AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_SESSION_TOKEN. This process is seamless and doesn’t require manual input of new credentials every time the role expires.
To ensure that temporary credentials are used correctly, Terraform needs to know where to look for them. This can be accomplished by defining the environment variables in the provider block or by configuring the environment outside of Terraform. If you use the AWS CLI or other AWS SDKs alongside Terraform, ensure that these temporary credentials are passed along and updated whenever they expire.
Terraform does not require manual updates to the credentials when the session expires. Instead, it will use the newly generated credentials automatically as long as the assume_role configuration is in place. This automation ensures that the process is both secure and seamless, reducing the administrative burden and the risk of using outdated or compromised credentials.
Managing Role Assumption in Multiple Environments
In large organizations, it is common to work with multiple AWS accounts or environments (e.g., production, staging, development). Managing least privileged access across these different environments can be challenging, but it is essential to maintain security while enabling the flexibility needed to manage infrastructure effectively.
Terraform makes it possible to manage role assumption across different environments by using separate configurations and AWS profiles. You can define different roles for each environment and configure Terraform to assume the correct role depending on the target environment.
For instance, you can define different provider blocks for each environment, each with its own assume_role configuration. For example:
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provider “aws” {
region = “us-east-1”
profile = “prod-profile”
assume_role {
role_arn = “arn:aws:iam::123456789012:role/terraform_prod_role”
session_name = “terraform_prod_session”
external_id = “prod-external-id”
}
}
provider “aws” {
region = “us-west-2”
profile = “dev-profile”
assume_role {
role_arn = “arn:aws:iam::987654321098:role/terraform_dev_role”
session_name = “terraform_dev_session”
external_id = “dev-external-id”
}
}
In this example, two separate providers are configured for the us-east-1 (production) and us-west-2 (development) regions. Each provider assumes a different role that grants permissions specific to that environment. This approach helps maintain least privilege by ensuring that each environment has its own set of permissions, reducing the risk of cross-environment security issues.
In multi-account setups, you can also take advantage of AWS Organizations to manage roles across multiple AWS accounts. By creating cross-account roles and using Terraform to assume these roles, you can control access to resources in different accounts while adhering to the principle of least privilege.
Using S3 Backend with Role Assumption
When managing Terraform state, it is common to use an S3 bucket to store the state files. This ensures that the state is persistent and accessible across multiple team members or automated pipelines. However, the access control around this S3 bucket must also be restricted using the least privilege principle.
To manage the Terraform state securely, you can create a dedicated role with limited permissions that only allows access to the S3 bucket for reading and writing state files. This role would not have permissions to modify or delete other resources in the AWS account.
For example, you can create a terraform_state role that only has the necessary permissions to interact with the S3 bucket and DynamoDB table (for state locking):
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resource “aws_iam_role” “terraform_state” {
name = “terraform_state_role”
assume_role_policy = jsonencode({
Version = “2012-10-17”
Statement = [
{
Action = “sts:AssumeRole”
Effect = “Allow”
Principal = {
Service = “ec2.amazonaws.com”
}
},
]
})
}
resource “aws_iam_policy” “terraform_state_policy” {
name = “terraform_state_policy”
policy = jsonencode({
Version = “2012-10-17”
Statement = [
{
Effect = “Allow”
Action = [“s3:GetObject”, “s3:PutObject”, “s3:ListBucket”]
Resource = [
“arn:aws:s3:::my-terraform-state-bucket/*”,
“arn:aws:s3:::my-terraform-state-bucket”
]
},
{
Effect = “Allow”
Action = “dynamodb:DescribeTable”
Resource = “arn:aws:dynamodb:us-east-1:123456789012:table/terraform-lock-table”
},
]
})
}
In this example, the role terraform_state is given limited permissions to interact with the S3 bucket that stores the Terraform state. This ensures that users who are working with Terraform state can only interact with the state files, not other resources in the AWS environment. By using role assumption in Terraform, you can ensure that even state management adheres to the principle of least privilege. Automating the process of role assumption and credential management is a crucial step in applying the principle of least privilege to your Terraform workflows in AWS. By using temporary security credentials and limiting permissions based on roles, you reduce the risk of exposure from long-lived credentials and mitigate potential security breaches. With Terraform’s ability to assume roles automatically, manage multiple environments, and integrate with S3 for state management, you can efficiently and securely manage your cloud infrastructure. The next step in ensuring a secure cloud environment is continuously reviewing and refining access policies and role definitions to stay ahead of evolving security challenges.
Fine-Tuning Permissions for Terraform with AWS Roles
In previous sections, we have discussed how to implement and automate the least privilege principle in AWS using Terraform and IAM roles. While role assumption is a powerful tool to enhance security, it’s crucial to fine-tune the permissions associated with these roles to further reduce the risk of over-permissioning. A key part of applying least privilege is ensuring that roles are as granular as possible and limited to only the necessary actions and resources.
This section will focus on techniques for refining role-based permissions, including best practices for permission granularity, policies, and how to ensure Terraform only has access to the exact resources it needs. Fine-tuning permissions can be challenging, but it is vital to the overall security posture of your cloud environment.
Defining Granular IAM Policies
The principle of least privilege is not just about using roles but also about creating granular IAM policies that restrict access to specific actions and resources. When defining IAM policies, it’s important to think about the “least required” permissions for each role based on the scope of actions that will be executed by Terraform.
For example, rather than assigning broad permissions like ec2:DescribeInstances or ec2:*, you can restrict the permissions to just the actions required to provision, modify, or delete EC2 instances. Using AWS-managed policies can provide a quick start, but customizing policies to your needs ensures that only the necessary permissions are granted.
Consider this example of a policy that grants permissions to launch and terminate EC2 instances but does not allow modification of other EC2 properties like security groups or key pairs:
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{
“Version”: “2012-10-17”,
“Statement”: [
{
“Effect”: “Allow”,
“Action”: [
“ec2:RunInstances”,
“ec2:TerminateInstances”
],
“Resource”: “arn:aws:ec2:us-east-1:123456789012:instance/*”
}
]
}
In this policy, we restrict the permissions to only actions required for managing EC2 instances. By specifying the actions (ec2:RunInstances and ec2:TerminateInstances) and the resources (arn:aws:ec2:us-east-1:123456789012:instance/*), we ensure that users or roles are not inadvertently granted permissions to modify other EC2 properties such as security groups, EBS volumes, or VPC settings.
By fine-tuning IAM policies in this way, you can limit access to only the actions that Terraform requires. You should review every policy to make sure that it grants only the permissions necessary for provisioning resources, while avoiding granting broader permissions that could increase your attack surface.
Leveraging Resource-Based Policies
While IAM policies grant permissions to users, roles, and services, another important mechanism in AWS for restricting access to resources is resource-based policies. These policies are attached directly to AWS resources and specify who can access the resource and what actions they can perform. Resource-based policies are particularly useful when you want to control access to specific resources, like an S3 bucket, Lambda function, or SNS topic, and ensure that only specific IAM roles or users can interact with them.
For example, consider an S3 bucket policy that only allows access to a specific role (or set of roles) to read or write to the bucket. Here’s an example of an S3 bucket policy that allows a specific role to interact with the bucket:
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{
“Version”: “2012-10-17”,
“Statement”: [
{
“Effect”: “Allow”,
“Action”: [
“s3:GetObject”,
“s3:PutObject”
],
“Resource”: “arn:aws:s3:::my-terraform-state-bucket/*”,
“Principal”: {
“AWS”: “arn:aws:iam::123456789012:role/terraform_state_role”
}
}
]
}
In this policy, we define that only the terraform_state_role has access to the S3 bucket my-terraform-state-bucket. By using resource-based policies, you can control access at the resource level rather than relying on broad IAM permissions, which might grant more access than necessary.
Using resource-based policies in combination with IAM policies ensures that permissions are tightly controlled at every layer, whether for managing resources or accessing critical state files. This is especially important when dealing with sensitive resources like S3 buckets, where state files could contain sensitive configuration data, passwords, or access keys.
Using IAM Conditions for Fine-Grained Control
IAM conditions offer a powerful way to enforce additional access restrictions based on specific criteria, such as IP address, time of day, or whether the request is coming from a specific VPC. This feature enables fine-grained control over permissions and can be used to further secure your infrastructure by limiting access to sensitive operations.
For example, consider the following policy that grants permission to assume a role only if the request comes from a specific IP address range:
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{
“Version”: “2012-10-17”,
“Statement”: [
{
“Effect”: “Allow”,
“Action”: “sts:AssumeRole”,
“Resource”: “arn:aws:iam::123456789012:role/terraform_provisioning”,
“Condition”: {
“IpAddress”: {
“aws:SourceIp”: “203.0.113.0/24”
}
}
}
]
}
In this example, the sts:AssumeRole action is granted only if the request originates from IP addresses within the 203.0.113.0/24 range. This additional layer of security ensures that even if credentials are compromised, the attacker will not be able to assume the role unless they are within the specified IP range.
Conditions can also be used to restrict access to certain times of day, for example, allowing role assumption only during business hours. This can be beneficial for managing access to sensitive resources or preventing unauthorized access during off-hours. By using conditions to limit the circumstances under which roles can be assumed, you can further refine your security posture.
Regularly Auditing and Reviewing Permissions
No security policy is set and forget. As organizations grow and evolve, the needs for different roles and permissions change. Regular auditing of IAM roles and policies is essential to maintaining a secure cloud environment. Terraform configurations, role assignments, and permissions should be reviewed periodically to ensure they still align with the principle of least privilege.
AWS provides several tools to assist with auditing permissions and configurations:
- IAM Access Analyzer: This tool helps you identify resources in your account that are shared with external entities. It can highlight potential misconfigurations or over-permissioning by analyzing IAM policies and resource-based policies.
- AWS CloudTrail: CloudTrail logs API activity across your AWS account, allowing you to track which users, roles, and services have accessed specific resources and what actions they performed. Regularly reviewing CloudTrail logs can help identify any unintended or unauthorized access.
- AWS Config: AWS Config can be used to assess, audit, and evaluate the configuration of your AWS resources. It helps you track changes to IAM policies, roles, and other configurations, ensuring that your security posture remains in compliance with your policies.
By leveraging these tools, you can identify and address any over-permissioned roles or misconfigured policies, ensuring that your least privilege strategy remains effective over time.
Implementing Role Separation for Increased Security
Another best practice when fine-tuning permissions is to implement role separation based on different environments and tasks. For example, an AWS environment might consist of various stages, such as development, staging, and production. Each stage may require different levels of access to resources.
Instead of using the same set of permissions across all environments, separate roles should be created for each environment. For instance, roles for the production environment should have stricter access controls than those for development. This ensures that even if a role in a less sensitive environment is compromised, the attacker cannot access production resources.
Consider a scenario where different roles are needed for managing resources in different environments:
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provider “aws” {
region = “us-east-1”
}
resource “aws_iam_role” “prod_role” {
name = “prod_role”
assume_role_policy = jsonencode({
Version = “2012-10-17”
Statement = [
{
Action = “sts:AssumeRole”
Effect = “Allow”
Principal = {
AWS = “arn:aws:iam::123456789012:role/terraform_prod_role”
}
}
]
})
}
resource “aws_iam_role” “dev_role” {
name = “dev_role”
assume_role_policy = jsonencode({
Version = “2012-10-17”
Statement = [
{
Action = “sts:AssumeRole”
Effect = “Allow”
Principal = {
AWS = “arn:aws:iam::123456789012:role/terraform_dev_role”
}
}
]
})
}
In this example, we create separate IAM roles for the production and development environments. These roles would be assigned different policies, granting the production environment stricter permissions compared to the development environment. This approach ensures that sensitive environments are protected by more stringent security measures.
Best Practices for Managing Least Privilege Access at Scale
In large cloud environments, managing least privileged access at scale can become complex. As your AWS infrastructure grows, so do the number of users, services, and roles that need precise access control. Maintaining effective least privilege principles requires ongoing vigilance, the adoption of scalable practices, and automation tools. This section will focus on best practices to manage access and permissions at scale while ensuring that your cloud environment remains secure and compliant.
Automating Role and Permission Management
When dealing with a large-scale AWS environment, manually managing roles and permissions for each user or service can quickly become unsustainable. To ensure that your access control remains consistent and secure, it’s essential to automate the process wherever possible. Terraform itself can be leveraged for automating the creation and management of IAM roles, policies, and permissions.
Using Infrastructure as Code (IaC) tools such as Terraform allows you to define IAM roles and policies programmatically, ensuring that permissions are granted in a repeatable and consistent manner. This approach reduces human error and ensures that your security policies remain up-to-date with minimal effort.
For instance, instead of manually creating IAM roles for each service or user, you can define them in Terraform configuration files, ensuring that all roles follow the same principles and standards. For example, a simple Terraform configuration to create an IAM role could look like this:
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resource “aws_iam_role” “terraform_provisioning” {
name = “terraform_provisioning”
assume_role_policy = jsonencode({
Version = “2012-10-17”
Statement = [
{
Action = “sts:AssumeRole”
Effect = “Allow”
Principal = {
Service = “ec2.amazonaws.com”
}
}
]
})
}
resource “aws_iam_policy” “terraform_provisioning_policy” {
name = “terraform_provisioning_policy”
policy = jsonencode({
Version = “2012-10-17”
Statement = [
{
Effect = “Allow”
Action = “ec2:RunInstances”
Resource = “*”
}
]
})
}
resource “aws_iam_role_policy_attachment” “attach_provisioning_policy” {
role = aws_iam_role.terraform_provisioning.name
policy_arn = aws_iam_policy.terraform_provisioning_policy.arn
}
In this example, a role terraform_provisioning is created, with a policy attached that grants permission to launch EC2 instances. By using Terraform, you can easily replicate this configuration across multiple services and environments, ensuring consistency and ease of management. Additionally, since Terraform is declarative, you can manage updates and changes by simply modifying the Terraform configuration and applying the changes across your infrastructure.
Automation also includes setting up Continuous Integration and Continuous Deployment (CI/CD) pipelines that incorporate security checks for permission management. These pipelines can help verify that permissions are correct and that least privilege is maintained as new roles or policies are introduced.
Adopting the Principle of Separation of Duties
One effective way to manage access at scale is by implementing the separation of duties principle. This principle ensures that critical tasks and permissions are divided between multiple roles or individuals, reducing the likelihood of unauthorized access or misuse. It also helps prevent a single point of failure or vulnerability in your access management strategy.
In AWS, this can be achieved by defining distinct roles for different operational tasks. For example, instead of giving one user full access to both the provisioning and management of resources, you can create separate roles for provisioning resources and managing them after they are created. These roles can then be assigned based on the specific needs of each user, task, or service.
A practical example of separation of duties would involve defining a terraform_admin role with permissions only for provisioning resources, and a terraform_operator role with permissions for ongoing management (such as stopping, restarting, or modifying existing resources). By doing this, you reduce the risk of accidental or malicious changes to critical resources, as the roles are strictly defined and aligned with responsibilities.
Separation of duties should also apply to different environments. For example, production resources should be handled by a separate team or role than development or staging environments. This further minimizes the risk of human error or malicious activity impacting critical production workloads.
Managing Cross-Account Access
As organizations expand their use of AWS and adopt a multi-account strategy, managing permissions across multiple accounts becomes essential. AWS Organizations allows you to centralize and manage multiple AWS accounts, providing a way to organize accounts into a hierarchical structure.
When dealing with cross-account access, it is important to implement strict role-based access control (RBAC) and avoid broad permissions. For example, you can create cross-account roles that grant limited access to resources in other AWS accounts, ensuring that each account only has access to the resources necessary for its purpose.
Consider the example where an application in one AWS account needs to access an S3 bucket in another account. You could create a cross-account IAM role in the S3 bucket’s account that grants permission to access the bucket, and the application’s IAM role in the application account could assume that role using sts:AssumeRole.
For example, the following role in the bucket owner’s account allows access from the application account:
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{
“Version”: “2012-10-17”,
“Statement”: [
{
“Effect”: “Allow”,
“Action”: [
“s3:GetObject”,
“s3:PutObject”
],
“Resource”: “arn:aws:s3:::my-shared-bucket/*”,
“Principal”: {
“AWS”: “arn:aws:iam::987654321098:role/application_role”
}
}
]
}
And in the application account, the role to assume the above permissions would look like this:
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resource “aws_iam_role” “application_role” {
name = “application_role”
assume_role_policy = jsonencode({
Version = “2012-10-17”
Statement = [
{
Action = “sts:AssumeRole”
Effect = “Allow”
Principal = {
AWS = “arn:aws:iam::123456789012:role/my-app-role”
}
}
]
})
}
This approach helps to maintain control over who has access to resources across AWS accounts, ensuring that permissions are applied based on roles rather than granting unrestricted access. Moreover, it allows teams to work across different accounts securely, ensuring that the principle of least privilege is enforced even in a multi-account setup.
Utilizing AWS Identity and Access Management (IAM) Access Analyzer
The AWS IAM Access Analyzer is a tool that helps you identify resources in your account that are shared with external entities. This tool is particularly valuable when scaling access management, as it helps you pinpoint over-permissioned resources, especially in complex environments where many roles and policies are in use.
By running IAM Access Analyzer periodically, you can identify any unintended access granted to external accounts or users, ensuring that your roles and policies adhere to the principle of least privilege. The tool will generate detailed findings on which resources are accessible externally and help you address security concerns before they become critical vulnerabilities.
To get started with IAM Access Analyzer, you can use the following steps:
- Enable the IAM Access Analyzer in your AWS account.
- Review the findings generated by the analyzer, which will show which resources are being shared with external entities.
- Apply necessary changes to resource-based policies or IAM roles to restrict unauthorized access.
Regularly using IAM Access Analyzer can help you proactively maintain secure and least-privileged access policies, especially when scaling your cloud infrastructure.
Monitoring and Logging Access Control Changes
As your AWS environment grows, it’s essential to monitor and log changes to your IAM roles, policies, and permissions. AWS provides several services to help track and audit access control modifications:
- AWS CloudTrail: CloudTrail logs API requests made to AWS services, including changes to IAM roles, policies, and permissions. By setting up CloudTrail, you can monitor who is making changes to your IAM configuration and what changes are being made.
- AWS Config: AWS Config helps you track configuration changes across your AWS resources, including IAM policies and roles. It provides a detailed history of changes, allowing you to compare configurations and identify any unauthorized or unexpected changes.
- CloudWatch Logs and Alarms: By integrating IAM activity with Amazon CloudWatch, you can set up alarms to notify you when critical IAM changes occur. For instance, you can be alerted if there is an increase in permissions for sensitive resources or if a new cross-account trust relationship is created.
With continuous monitoring and logging, you can maintain visibility over your access control policies and quickly respond to any unauthorized or unexpected changes, ensuring that the least privilege is consistently enforced.
Conclusion
Managing least privileged access in AWS at scale requires a combination of automation, best practices, and continuous monitoring. By adopting a granular approach to permissions, separating duties, and leveraging tools such as IAM Access Analyzer and AWS CloudTrail, you can ensure that your AWS infrastructure remains secure and compliant. Furthermore, by automating role creation and policy management with Terraform, you can ensure that your least privilege access control practices are consistent and repeatable, even as your cloud environment grows. The journey to effective security is ongoing, and by implementing these practices, you can reduce the risk of unauthorized access and protect your resources from potential threats.