Fine-Grained Permissions for Model Deployment with AWS IAM Policy

Question

Pulumi · Accepted Answer

To set up fine-grained permissions for model deployment with an AWS IAM Policy, we'll take the following steps in our Pulumi program:

1. Define an IAM Policy that outlines the specific actions and resources the policy allows or denies. This is done by creating the policy document, which is a JSON structure defining the policy.

2. Once we have our policy document, we create an IAM Policy resource using the AWS IAM module in Pulumi.

3. Optionally, we can attach this policy to an IAM Role that an AWS service or user assumes to get the permissions defined by the policy. This allows us to grant the permissions only to entities that need it, following the principle of least privilege.

Let's write a Pulumi program in Python that demonstrates how to create an IAM Policy with fine-grained permissions for model deployment:

```python
import pulumi
import pulumi_aws as aws

# Step 1: Define the policy document
# This policy document allows actions on specific AWS S3 buckets and SageMaker endpoints.
# You can modify this according to the specific resources and actions needed for your use case.
model_deployment_policy_document = aws.iam.get_policy_document(statements=[
    {
        "effect": "Allow",
        "actions": ["sagemaker:CreateEndpoint", "sagemaker:DeleteEndpoint", "sagemaker:InvokeEndpoint"],
        "resources": ["arn:aws:sagemaker:us-west-2:123456789012:endpoint/your-sagemaker-endpoint"]
    },
    {
        "effect": "Allow",
        "actions": ["s3:GetObject", "s3:PutObject"],
        "resources": ["arn:aws:s3:::your-model-bucket/*"]
    }
])

# Step 2: Create an IAM Policy with the policy document
model_deployment_policy = aws.iam.Policy("modelDeploymentPolicy",
    description="Policy to permit specific operations on S3 and SageMaker for model deployment",
    policy=model_deployment_policy_document.json
)

# Optionally, create an IAM Role and attach the policy to it.
# Note: The Trust Relationship Policy is just an example here. You would configure it as per your use case.
model_deployment_role = aws.iam.Role("modelDeploymentRole",
    assume_role_policy={
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "sagemaker.amazonaws.com"
            }
        }]
    }
)

# Attach the policy to the role
model_deployment_role_policy_attachment = aws.iam.RolePolicyAttachment("modelDeploymentRolePolicyAttachment",
    role=model_deployment_role.name,
    policy_arn=model_deployment_policy.arn
)

# Export the ARN of both the policy and the role
pulumi.export("model_deployment_policy_arn", model_deployment_policy.arn)
pulumi.export("model_deployment_role_arn", model_deployment_role.arn)
```

This program defines an IAM policy with permissions for specific SageMaker actions and S3 operations related to model deployment. The policy is then attached to a role that could be assumed by AWS services or users that need to perform model deployment. The program assumes a SageMaker endpoint ARN and an S3 bucket ARN; you should replace these with the actual ARNs required for your model deployment.

By limitings permissions with these fine-grained controls, you can follow best security practices by ensuring that each service or user in your AWS environment has only the permissions necessary to perform their intended tasks.