1. Centralized Control of AI Data Across S3 Buckets


    To achieve centralized control of AI data across S3 buckets in AWS, you'll need to create multiple S3 buckets and configure various aspects like access control, bucket policies, and potentially replication if you need the data to be present in multiple regions for compliance or latency reasons.

    I'll guide you through a program that:

    1. Creates S3 buckets that will hold your AI data.
    2. Applies Bucket Policies to manage permissions.
    3. Configures Bucket Ownership Controls for Access Control Lists (ACLs).
    4. Enables Public Access Blocks to prevent public access.
    5. Sets up Bucket Replication if needed to replicate data across different AWS Regions.

    This program assumes that you have AWS configured with the necessary permissions to create and manage S3 resources.

    Here's the Pulumi program that accomplishes this:

    import pulumi import pulumi_aws as aws # Creates a new S3 bucket for storing AI data with versioning enabled ai_data_bucket = aws.s3.Bucket("aiDataBucket", versioning=aws.s3.BucketVersioningArgs( enabled=True, )) # Applies a Bucket Policy to enforce specific permissions bucket_policy = aws.s3.BucketPolicy("aiDataBucketPolicy", bucket=ai_data_bucket.id, policy=ai_data_bucket.arn.apply(lambda arn: { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": "*", "Action": "s3:GetObject", "Resource": f"{arn}/*", "Condition": { "StringEquals": { "s3:RequestedRegion": "us-west-2" # Replace with your desired Region } } }] })) # Configures the ownership controls on the bucket bucket_ownership_controls = aws.s3.BucketOwnershipControls("aiDataBucketOwnershipControls", bucket=ai_data_bucket.id, rule=aws.s3.BucketOwnershipControlsRuleArgs( object_ownership="BucketOwnerPreferred", )) # Blocks public access to the bucket public_access_block = aws.s3.BucketPublicAccessBlock("aiDataBucketAccessBlock", bucket=ai_data_bucket.id, block_public_acls=True, block_public_policy=True, ignore_public_acls=True, restrict_public_buckets=True) # If you have multiple buckets and want to replicate the data between them # First, you need a role for the replication configuration replication_role = aws.iam.Role("replicationRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "s3.amazonaws.com" }, "Action": "sts:AssumeRole" } ] }""") # Then, create an S3 bucket for the replicated data replicated_data_bucket = aws.s3.Bucket("replicatedDataBucket") # Finally, attach the replication configuration to the original bucket replication_config = aws.s3.BucketReplicationConfig("aiDataBucketReplicationConfig", bucket=ai_data_bucket.id, role=replication_role.arn, rules=[aws.s3.BucketReplicationConfigRuleArgs( id="replicationRule", status="Enabled", destination=aws.s3.BucketReplicationConfigRuleDestinationArgs( bucket=replicated_data_bucket.arn, storage_class="STANDARD", ), )]) # Export the bucket names and URLs pulumi.export("ai_data_bucket_name", ai_data_bucket.id) pulumi.export("replicated_data_bucket_name", replicated_data_bucket.id)

    Here's a breakdown of what we're doing in this program:

    • aws.s3.Bucket(...): This creates a new S3 bucket in AWS.
    • aws.s3.BucketPolicy(...): This attaches a policy to the bucket. The policy can be configured to enforce specific permissions based on your use case.
    • aws.s3.BucketOwnershipControls(...): This configures the ownership controls of the bucket to determine how ACLs are managed.
    • aws.s3.BucketPublicAccessBlock(...): This ensures that the bucket does not allow public access, keeping your data private.
    • Bucket Replication: This part is optional and is used when there is a need to replicate the data across different AWS regions. It requires creating a replication role and attaching a replication configuration to your bucket.

    If you have any more specific needs or questions regarding managing AWS S3 buckets for AI data or any other Pulumi-related inquiries, feel free to ask!