Secure Access to Training Data with AWS IAM Roles

Question

Pulumi · Accepted Answer

To secure access to training data with AWS IAM roles, you need to create an IAM policy that defines the permissions necessary to access the training data, and then attach this policy to an IAM role. This role can then be assumed by the entities (like AWS services or users) that need to work with the training data.

Below, I'm providing a Pulumi program that creates an S3 bucket to store training data, an IAM policy with read-only access to that bucket, and an IAM role that can be assumed by an AWS service (like AWS EC2 or AWS Lambda) which will use that data for training purposes.

1. An S3 bucket is created to store the training data.
2. An IAM policy is defined that specifies read-only access to the S3 bucket.
3. An IAM role is created, which can be assumed by other AWS services.
4. The IAM policy is attached to the IAM role, granting the permissions defined in the policy to any entity assuming the role.

Here's the Pulumi Python program to achieve this:

```python
import pulumi
import pulumi_aws as aws

# Create an S3 bucket to store the training data
training_data_bucket = aws.s3.Bucket("trainingDataBucket")

# Define an IAM policy for read-only access to the training data bucket
training_data_policy_document = aws.iam.get_policy_document(
    statements=[aws.iam.GetPolicyDocumentStatementArgs(
        actions=["s3:GetObject"],
        resources=[training_data_bucket.arn.apply(lambda arn: f"{arn}/*")],
    )]
)

training_data_policy = aws.iam.Policy("trainingDataPolicy",
    policy=training_data_bucket.arn.apply(lambda arn: training_data_policy_document.json)
)

# Create an IAM role that services like EC2 or Lambda can assume to access the training data
training_data_role = aws.iam.Role("trainingDataRole",
    assume_role_policy=aws.iam.get_policy_document(
        statements=[aws.iam.GetPolicyDocumentStatementArgs(
            actions=["sts:AssumeRole"],
            principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
                type="Service",
                identifiers=["ec2.amazonaws.com"]  # Example service that might be performing training
            )]
        )]
    ).json
)

# Attach the IAM policy to the IAM role
policy_attachment = aws.iam.RolePolicyAttachment("trainingDataPolicyAttachment",
    role=training_data_role.name,
    policy_arn=training_data_policy.arn
)

# Export the names and ARNs of the created resources
pulumi.export("training_data_bucket_name", training_data_bucket.id)
pulumi.export("training_data_policy_arn", training_data_policy.arn)
pulumi.export("training_data_role_arn", training_data_role.arn)
```

In this program, you first specify an S3 bucket where the training data will be stored. Next, you define an IAM policy with the necessary permissions to read objects from that bucket. To grant these permissions to other AWS services, you create an IAM role. This role has a trust policy that allows it to be assumed by a specified AWS service, in this case, 'ec2.amazonaws.com'. After creating the role, you attach the read-only policy to it with a `RolePolicyAttachment`. Finally, you export some of the relevant details of the resources created, such as the S3 bucket name and the ARNs of the IAM policy and role, which can be useful for other parts of your AWS setup.

By following these steps, you can ensure secured, controlled access to your training data using AWS IAM roles and policies with Pulumi.