1. Secure Data Sharing for AI with Dataset IAM Policies


    To implement secure data sharing for AI with Dataset IAM Policies, we'll focus on using cloud provider resources to manage access to datasets, particularly in a cloud-based environment where datasets may be used for AI and machine learning purposes.

    Let's use Google Cloud Platform (GCP) as an example here, as it's one of the major cloud providers offering extensive AI and machine learning capabilities and it is reflected in the Pulumi Registry results. Specifically, we will use the DatasetIamPolicy in the google-native provider which is designed for managing Identity and Access Management (IAM) policies for Google Cloud Healthcare datasets. IAM policies define permissions for who (users, groups, service accounts, etc.) can do what (e.g., read, write, admin) on a particular resource.

    In Google Cloud, a dataset in the healthcare API can contain sensitive patient information, hence, securing access to these datasets through IAM policies is critical for compliance and data protection. By managing IAM policies, you can control access to the healthcare dataset and allow for secure data sharing with AI applications or other analytics tools.

    Here's a program written in Python using Pulumi that sets up a healthcare dataset and applies an IAM policy to it. The IAM policy specifies roles and members that are authorized to interact with the dataset.

    import pulumi import pulumi_google_native.healthcare.v1 as healthcare # Replace these variables with the appropriate values. project_id = 'your-gcp-project-id' location = 'gcp-region-or-zone' dataset_id = 'your-dataset-id' # Create a Google Cloud Healthcare Dataset. dataset = healthcare.Dataset("my-dataset", dataset_id=dataset_id, project=project_id, location=location) # Define the IAM policy for the dataset to specify access control. # The roles and members should be modified according to your requirements. # Here we are giving the 'roles/healthcare.datasetViewer' role to a 'user' # and 'serviceAccount' which can be as per the need for the AI application. iam_policy = healthcare.DatasetIamPolicy("my-dataset-iam-policy", dataset_id=dataset.id, project=project_id, location=location, bindings=[{ "role": "roles/healthcare.datasetViewer", "members": [ "user:example-user@domain.com", "serviceAccount:example-sa@project-id.iam.gserviceaccount.com" ] }]) # Export the dataset name and IAM policy id pulumi.export('dataset_name', dataset.name) pulumi.export('iam_policy_id', iam_policy.id)

    In this program:

    • We first declare a Dataset in the Google Cloud Healthcare API. This dataset will be used to store healthcare-related information that you may want to analyze or utilize in AI models.
    • The second resource is the DatasetIamPolicy, which configures who can access this dataset. We apply an IAM policy to the dataset, specifying the roles and members. In the example, we provide viewer access to a specified user and a service account. In practice, you would adjust these roles and members according to your organization's access policies and requirements.
    • Lastly, we use pulumi.export to output the names of our created resources, which can be useful for debugging and for referencing these resources elsewhere.

    You would need to update project_id, location, dataset_id, and IAM policy members with values relevant to your setup. When you run this Pulumi program with pulumi up, it will deploy these resources in your GCP account, following the specified configurations.