1. AI Model Training with Encrypted Data in Vault


    To address the goal of AI model training with encrypted data in a vault, we need to take into account the security and management of the sensitive data used during the training process. Encrypted data storage typically involves a key management service, which securely stores and controls the access to encryption keys.

    For our use case, we can utilize cloud services like AWS Key Management Service (KMS) for key management. KMS provides a centralized control over the cryptographic keys and integrates with other AWS services to enable encryption of data at rest.

    Suppose we are processing data for machine learning (ML) within the AWS cloud environment. We would store the encrypted data in an Amazon S3 bucket, using server-side encryption with an AWS KMS key. The ML model training can be set up using Amazon SageMaker, which provides an integrated environment to prepare, build, train, and deploy ML models at scale.

    Here is a high-level overview of the steps we will be taking in the Pulumi program:

    1. Create a KMS key for encrypting our data in S3.
    2. Create an S3 bucket with server-side encryption enabled, using the KMS key.
    3. Upload the encrypted training data to the S3 bucket.
    4. Set up an SageMaker training job, specifying the S3 bucket as our data source.

    Let's go through a Pulumi Python program that implements these steps:

    import pulumi import pulumi_aws as aws # Create a KMS key for encrypting our data kms_key = aws.kms.Key("aiModelTrainingKey", description="KMS key for AI model training data", policy="""{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": {"AWS": "arn:aws:iam::<ACCOUNT_ID>:root"}, "Action": "kms:*", "Resource": "*" }] }""" ) # Create an S3 bucket with encryption enabled using the KMS key s3_bucket = aws.s3.Bucket("aiModelTrainingDataBucket", acl="private", server_side_encryption_configuration={ "rule": { "applyServerSideEncryptionByDefault": { "sseAlgorithm": "aws:kms", "kmsMasterKeyId": kms_key.id, } } } ) # Assuming we have data to train our AI model that's already encrypted, # we will upload this data to our S3 bucket # Sample file to demonstrate - replace `path` with the actual path to your encrypted data file encrypted_data_file = aws.s3.BucketObject("encryptedData", bucket=s3_bucket.id, key="path/to/encrypted-data.zip", source=pulumi.FileAsset("path/to/encrypted-data.zip") ) # Set up the SageMaker training job # Note: This code assumes you have already set up the SageMaker environment with a training image and role sagemaker_training_job = aws.sagemaker.TrainingJob("aiModelTrainingJob", # ... SageMaker Training Job configuration ... # Specify the S3 bucket as the input data location # and other training-specific parameters ) pulumi.export("kms_key_id", kms_key.id) pulumi.export("s3_bucket_name", s3_bucket.id)

    This code sets up the necessary AWS resources for storing and accessing encrypted training data using Pulumi. The AWS KMS key is created with a policy that grants full access to the root user of your AWS account. The S3 bucket is created with server-side encryption enabled, specifying the KMS key as the encryption method.

    Remember to replace path/to/encrypted-data.zip and <ACCOUNT_ID> with your actual data path and AWS account ID, respectively. Additional SageMaker training job details should be filled in at the aws.sagemaker.TrainingJob resource instantiation - this would include things like the training job name, machine learning model hyperparameters, the S3 bucket as the source of the training data, and the role that SageMaker should assume when running the job.

    Please consult the AWS documentation for detailed configuration parameters for KMS keys, S3 buckets with encryption, and SageMaker training jobs, and read Pulumi's AWS documentation to understand how to deploy these resources with Pulumi.