Fine-grained Access Control for AI Model Building with AWS Identity Store

Question

Pulumi · Accepted Answer

When building AI models on AWS, it is critical to establish fine-grained access control to protect sensitive data and resources. AWS Identity Store, along with other AWS services, can be utilized to manage identities and permissions effectively.

Here's a high-level overview of how you can use Pulumi to set up a fine-grained access control system for a scenario where you might be building AI models on AWS:

1. **AWS Identity Store**: This service can be used to manage identities that need access to AWS resources.

2. **Group Membership**: You can define groups in AWS Identity Store and add users to these groups. This way you can manage permissions at the group level rather than at the individual user level, which simplifies the management process.

3. **IAM Roles and Policies**: Using AWS Identity and Access Management (IAM), you can define roles with specific permissions and assign these roles to groups or individual users in AWS Identity Store.

Let's create a simple Pulumi Python program that demonstrates setting up a group and assigning a specific policy to that group which could be used in an AI model building scenario.

Below is the program:

```python
import pulumi
import pulumi_aws as aws

# Create an IAM role with a trust policy for AWS Identity Store
ai_model_builder_role = aws.iam.Role("aiModelBuilderRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [
            {
                "Action": "sts:AssumeRole",
                "Effect": "Allow",
                "Principal": {"Service": "sagemaker.amazonaws.com"}
            }
        ]
    }"""
)

# Attach a policy to the IAM role that gives necessary permissions for AI model building
ai_model_builder_policy = aws.iam.Policy("aiModelBuilderPolicy",
    policy="""{
        "Version": "2012-10-17",
        "Statement": [
            {
                "Action": [
                    "sagemaker:*",
                    "ecr:GetAuthorizationToken",
                    "ec2:CreateNetworkInterface",
                    "ec2:DeleteNetworkInterface",
                    "ec2:DescribeNetworkInterfaces",
                    "logs:*"
                ],
                "Resource": "*",
                "Effect": "Allow"
            }
        ]
    }"""
)

# Attach the policy to the role
ai_model_builder_role_policy_attachment = aws.iam.RolePolicyAttachment("aiModelBuilderRolePolicyAttachment",
    role=ai_model_builder_role.name,
    policy_arn=ai_model_builder_policy.arn
)

# Create a group in AWS Identity Store
ai_model_builders_group = aws.identitystore.Group("aiModelBuildersGroup",
    description="Group for AI model builders"
)

# In a real-world scenario, you'd dynamically reference the group ID and user ID
# For demonstration purposes, we will use hardcoded 'exampleGroupId' and 'exampleUserId'
example_group_membership = aws.identitystore.GroupMembership("exampleGroupMembership",
    group_id="exampleGroupId",   # Replace with actual group ID
    member_id="exampleUserId"    # Replace with actual member ID
)

# Output the ARN of the IAM role created
pulumi.export("ai_model_builder_role_arn", ai_model_builder_role.arn)
```

In this program:

- We create an `IAM Role` named `aiModelBuilderRole` that AI services like Amazon SageMaker can assume. The trust policy indicates which entities can assume the role—in this case, the SageMaker service.
  
- Then we create an `IAM Policy` named `aiModelBuilderPolicy` that outlines the permissions required for building AI models. This includes permissions for SageMaker, ECR (for working with container images), EC2 (for managing network interfaces that may be needed by SageMaker), and CloudWatch Logs (for logging).

- We attach the policy to our role using an `IAM Role Policy Attachment`.

- Next, we set up a `Group` in AWS Identity Store with a description indicating that it's for AI model builders.

- Then we demonstrate adding a user to this group using `Group Membership`. Replace `exampleGroupId` and `exampleUserId` with the actual group and user IDs from AWS Identity Store.

At the end, the program exports the ARN (Amazon Resource Name) of the IAM role created for use in other parts of your infrastructure as needed.

This is a foundational setup for access control in AI model building with AWS Identity Store, and you can extend this with additional users, groups, permissions, and policies as per your requirements.