1. AI Model Data Leakage Prevention with S3 Access Blocks


    To prevent data leakage when hosting AI models and datasets on AWS S3, you can apply specific access blocks to prevent public access to S3 buckets. AWS provides functionalities such as BucketPublicAccessBlock to manage the access settings of an AWS S3 bucket, allowing you to set parameters that block public access.

    Here's what these properties do:

    • blockPublicAcls: Ensures that any public ACLs aren't applied to this S3 bucket.
    • ignorePublicAcls: Ignores public ACLs on this S3 bucket and any objects it contains.
    • blockPublicPolicy: Prevents the bucket policy from being set to public.
    • restrictPublicBuckets: Restricts access to this bucket to only AWS Service Principals and authorized users within the bucket owner's account.

    By setting these properties to True, you impose strong access restrictions that are generally recommended for sensitive AI model data.

    Below is a Pulumi program written in Python that shows how to use these settings for a new S3 bucket:

    import pulumi import pulumi_aws as aws # Create an AWS S3 bucket that will store the AI models and datasets ai_data_bucket = aws.s3.Bucket("aiDataBucket", acl="private", # Start with a private ACL ) # Apply the public access block configuration to the S3 bucket bucket_access_block = aws.s3.BucketPublicAccessBlock("aiDataBucketAccessBlock", bucket=ai_data_bucket.id, # Reference the bucket's ID block_public_acls=True, ignore_public_acls=True, block_public_policy=True, restrict_public_buckets=True, ) # To interact with this program, you can export the bucket's name and/or ARN pulumi.export('bucket_name', ai_data_bucket.bucket) # The name of the bucket pulumi.export('bucket_arn', ai_data_bucket.arn) # The ARN of the bucket pulumi.export('access_block_id', bucket_access_block.id) # The ID of the Public Access Block settings

    This program starts by importing the necessary Pulumi AWS modules. It then creates a new private S3 bucket designed for storing AI datasets and models. After creating the bucket, we define and attach a BucketPublicAccessBlock setting to ensure that the data in the bucket is not accidentally exposed to the public.

    Exported values at the end, such as bucket_name, bucket_arn, and access_block_id, allow you to retrieve important information from the Pulumi stack, which can be useful for debugging purposes or for integration with other systems or components.