Secure AI Dataset Sharing with Multi-Origin Policies

Question

Pulumi · Accepted Answer

Securely sharing AI datasets across multiple origins typically involves two key components: controlling access to the dataset and managing cross-origin resource sharing (CORS) policies that allow web applications from various origins to access the data.

To accomplish this, you should set up these policies within a cloud environment that hosts the AI datasets. For simplicity, let's focus on setting up a secure AI dataset sharing mechanism on Azure, leveraging Azure Machine Learning services and Azure storage blobs with CORS.

Here's how you would achieve it with Pulumi and Python:

1. You would first create an Azure Machine Learning workspace, which allows you to manage the lifecycle of your AI models and datasets.
2. You would create a dataset within this workspace with the required access policies.
3. You would then configure the CORS settings for the Azure Storage Account to allow access from the specified origins.

Below is a Pulumi program written in Python that sets up these resources on Azure. The program:
- Creates an Azure Machine Learning workspace.
- Defines a Machine Learning dataset.
- Sets a CORS policy on an Azure Storage Blob.

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure Resource Group
resource_group = azure_native.resources.ResourceGroup(
    "resource_group",
    resource_group_name="ai_datasets_resource_group"
)

# Create an Azure Machine Learning Workspace
ml_workspace = azure_native.machinelearningservices.Workspace(
    "ml_workspace",
    resource_group_name=resource_group.name,
    workspace_name="secure_ai_workspace",
    sku=azure_native.machinelearningservices.SkuArgs(
        name="Basic"
    ),
    location=resource_group.location
)

# Define an Azure Machine Learning Dataset (assuming you have some dataset details)
# This dataset would be abiding by the stored access policy and linked to the ML workspace.
# For full configuration refer to the specific properties needed to create an ML dataset:
# https://www.pulumi.com/registry/packages/azure-native/api-docs/machinelearningservices/machinelearningdataset/
ml_dataset = azure_native.machinelearningservices.MachineLearningDataset(
    "ml_dataset",
    resource_group_name=resource_group.name,
    workspace_name=ml_workspace.name,
    dataset_name="secure_ai_dataset"
)

# Update Azure Storage Account with CORS rules
# This configuration allows for multi-origin access to the storage account where the datasets are located.
# You would add the origins you want to allow, methods (e.g., GET, POST..), and other CORS settings.
storage_account = azure_native.storage.StorageAccount(
    "storage_account",
    resource_group_name=resource_group.name,
    account_name="aiseccurestor",
    kind="StorageV2",
    sku=azure_native.storage.SkuArgs(
        name="Standard_LRS"
    ),
    location=resource_group.location,
    cors_rules=azure_native.storage.CorsRulesArgs(
        cors_rules=[
            azure_native.storage.CorsRuleArgs(
                allowed_origins=["http://www.example1.com", "http://www.example2.com"],
                allowed_methods=["GET", "POST"],
                max_age_in_seconds=3600,
                exposed_headers=["x-custom-header"],
                allowed_headers=["*"]
            )
        ]
    )
)

# Export the endpoint for access
pulumi.export("ml_workspace_url", ml_workspace.workspace_url)
pulumi.export("storage_account_primary_endpoint", storage_account.primary_endpoints)
```

This program does the following:
- It creates a new resource group to hold all the resources.
- It establishes a new Azure Machine Learning workspace, which can be used to manage and share AI datasets.
- It then creates a Machine Learning dataset within this workspace.
- Finally, it updates the Azure Storage Account where the AI datasets are stored with CORS rules to allow specific origins to access the data securely.

By employing Azure Machine Learning and Azure Storage services, this Pulumi program enables a structured and secure environment capable of sharing AI datasets with fine-grained control over access and policies.

Remember, when using Pulumi, you will need to have the Azure CLI installed and logged in to the account with the necessary permissions to create these resources. After writing this code in a file named `__main__.py`, run it with the `pulumi up` command to deploy the resources to Azure.