Encrypted Data Transfers in Azure ML Workspaces.

Question

Pulumi · Accepted Answer

When dealing with cloud resources, especially in areas such as machine learning and data processing, security is a paramount concern. Azure Machine Learning (Azure ML) is a cloud service that helps you manage, deploy, and version your machine learning models. One of the security aspects of Azure ML is ensuring that data transfers into and out of Azure ML workspaces are encrypted to protect the data from unauthorized access.

Encryption in transit protects your data if someone manages to intercept it as it travels from one point to another. Azure ML supports encryption in transit by default by using HTTPS endpoints for its APIs which help in transferring data securely. However, when setting up a workspace and associated resources, you have to ensure that encryption at rest is also configured according to your security needs.

In Pulumi, you can define the Azure ML workspace and its associated resources using the `azure_native.machinelearningservices.Workspace` class from the `pulumi_azure_native` provider. This class supports configuring various properties, including encryption settings.

Below is a Pulumi program that sets up an Azure ML Workspace with encryption for data at rest enabled. The encryption is provided through Azure Key Vault, where you store and manage your encryption keys securely.

```python
import pulumi
import pulumi_azure_native as azure_native
from pulumi_azure_native import machinelearningservices
from pulumi_azure_native import keyvault

# Create an Azure Resource Group
resource_group = azure_native.resources.ResourceGroup('resource_group')

# Create an Azure KeyVault to store the encryption keys securely
key_vault = keyvault.Vault('vault',
    resource_group_name=resource_group.name,
    properties=keyvault.VaultPropertiesArgs(
        sku=keyvault.SkuArgs(
            family='A',
            name='standard',
        ),
        tenant_id='your-tenant-id',
        access_policies=[],
        enabled_for_deployment=True,
        enabled_for_disk_encryption=True,
        enabled_for_template_deployment=True,
    )
)

# Create an Azure KeyVault Key for encryption
key = keyvault.Key("key",
    key_name="workspace-encryption-key",
    resource_group_name=resource_group.name,
    vault_name=key_vault.name,
    properties=keyvault.KeyPropertiesArgs(
        key_size=2048,
        key_type="RSA",
        key_opts=["encrypt", "decrypt", "sign", "verify", "wrapKey", "unwrapKey"],
    )
)

# Create an Azure ML Workspace with encryption at rest using the above key
ml_workspace = machinelearningservices.Workspace('mlWorkspace',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    properties=machinelearningservices.WorkspacePropertiesArgs(
        sku=machinelearningservices.SkuArgs(
            name='Basic',
        ),
        key_vault=key_vault.properties.vault_uri,
        application_insights='/subscriptions/your-subscription-id/resourcegroups/resource-group/providers/microsoft.insights/components/your-application-insights',
        container_registry='/subscriptions/your-subscription-id/resourcegroups/resource-group/providers/microsoft.containerregistry/registries/your-acr',
        storage_account='/subscriptions/your-subscription-id/resourcegroups/resource-group/providers/microsoft.storage/storageaccounts/your-storage-account',
        identity=machinelearningservices.IdentityConfigurationArgs(
            type="SystemAssigned"
        ),
        encryption=machinelearningservices.EncryptionPropertyArgs(
            status="Enabled",
            key_vault_properties=machinelearningservices.KeyVaultPropertiesArgs(
                key_identifier=key.id,
                identity_client_id=ml_workspace.identity.apply(lambda id: id.principal_id if id else None)
            )
        )
    )
)

# Exporting the Azure ML Workspace URL
pulumi.export('ML Workspace', ml_workspace.name)
pulumi.export('ML Workspace URL', ml_workspace.discovery_url)
```

First, the code creates an Azure resource group that provides a namespace for grouping related resources. Then, it creates an Azure KeyVault for secure key management, followed by an Azure KeyVault Key that will be used to encrypt data at rest.

Next, we define the Azure ML Workspace, setting its encryption properties to use the key we created and stored in Azure KeyVault and enabling encryption at rest.

Lastly, we export the Azure ML Workspace URL, which you can use to access the Azure ML Studio and manage your machine learning workflows.

Remember to replace placeholders like `your-tenant-id`, `your-subscription-id`, `resource-group`, and other similar placeholders with actual values from your Azure subscription and resources.

This program ensures that any data saved in the workspace's associated storage account is encrypted using the specified key from Azure KeyVault. It's essential for maintaining secure machine learning operations in the cloud.