- Knowledge Base
- Microsoft Azure
- Machine Learning
- Enable High Business Impact for Machine Learning Workspaces
Ensure that High Business Impact (HBI) feature is enabled for Azure Machine Learning (ML) workspaces that contains sensitive data in order to limit the data that Microsoft Azure collects on your workspaces for diagnostic purposes.
High Business Impact (HBI) prevents sending confidential telemetry data to Microsoft and enables additional encryption in Microsoft managed environments. Enabling HBI ensures that appropriate security measures, compliance requirements, and risk management protocols are in place to protect sensitive and critical data, thereby safeguarding the organization's interests and maintaining trust.
Audit
To determine if the High Business Impact feature is enabled for your Azure Machine Learning workspaces, perform the following actions:
Using Azure Console
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Sign in to the Azure Management Console.
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Navigate to Azure Machine Learning workspaces blade at https://portal.azure.com/#browse/Microsoft.MachineLearningServices%2Fworkspaces.
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Select the Azure subscription that you want to examine from the Subscription filter box.
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Click on the name (link) of the Azure Machine Learning workspace that you want to examine.
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In the main navigation panel, select Overview, and choose JSON View under Essentials to access the workspace configuration information available in JSON format.
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On the Resource JSON panel, check for the "properties.hbiWorkspace" configuration attribute value in the resource configuration object, to determine the High Business Impact feature status for the selected workspace. If the "properties.hbiWorkspace" attribute value is set to false, the High Business Impact (HBI) feature is not enabled for the selected Azure Machine Learning workspace.
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Repeat steps no. 4 – 6 for each Azure Machine Learning workspace available within the selected subscription.
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Repeat steps no. 3 – 7 for each subscription created in your Microsoft Azure cloud account.
Using Azure CLI
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Run ml workspace list command (Windows/macOS/Linux) with custom query filters to list the name and the associated resource group for each Azure Machine Learning workspace available in the current subscription:
az ml workspace list --output table --query '[*].{name:name, resourceGroup:resourceGroup}' -
The command output should return the requested Azure Machine Learning workspace identifiers:
Name ResourceGroup ------------------------ ------------------------------ tm-ml-project5-workspace cloud-shell-storage-westeurope tm-ml-production-workspace cloud-shell-storage-westeurope
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Run ml workspace show command (Windows/macOS/Linux) with the name of the Azure Machine Learning workspace that you want to examine as the identifier parameter and custom output filters to describe the High Business Impact feature status for the selected workspace:
az ml workspace show --name tm-ml-project5-workspace --resource-group cloud-shell-storage-westeurope --query 'hbi_workspace'
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The command output should return the requested feature status:
false
If the ml workspace show command output returns false, as shown in the example above, the High Business Impact (HBI) feature is not enabled for the selected Azure Machine Learning workspace.
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Repeat step no. 3 and 4 for each Azure Machine Learning workspace available in the selected Azure subscription.
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Repeat steps no. 1 – 5 for each subscription created in your Microsoft Azure cloud account.
Remediation / Resolution
To enable High Business Impact (HBI) for your Azure Machine Learning (ML) workspaces, you must re-create your workspaces with the necessary HBI configuration, by performing the following operations:
Using Azure Console
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Sign in to the Azure Management Console.
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Navigate to Azure Machine Learning workspaces blade at https://portal.azure.com/#browse/Microsoft.MachineLearningServices%2Fworkspaces.
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Select the Azure subscription that you want to access from the Subscription equals all filter box, and choose Apply.
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Choose Create, select New workspace, and perform the following actions to create your new Azure Machine Learning workspace:
- For Basics, provide the following information:
- For Subscription, choose your Azure subscription.
- For Resource group, select the correct resource group.
- Provide a unique name for the workspace in the Name box.
- For Region, select the Azure cloud region where the workspace will be deployed.
- For Storage account, choose the storage account that is used as the default datastore for the workspace.
- For Key vault, select the key vault used to store secrets and other sensitive information required by the workspace.
- For Application insights, choose whether to enable the Application Insights monitoring feature. You can create a new Azure Application Insights resource or select an existing one from your subscription.
- For Container registry, select the container registry used to register docker images used in training and deployments.
- Choose Next : Networking to continue the workspace setup process.
- For Networking, choose the type of network isolation that you need for your workspace. Choose Next : Encryption to continue the setup.
- For Encryption, perform the following actions:
- For Encryption type, choose Customer-managed keys to use your own Customer-Managed Key (CMK) for data encryption (recommended).
- For Key vault, choose Click to select the key, and select the appropriate key vault and Customer-Managed Key (CMK).
- Choose Next : Identity to continue.
- For Identity, perform the following operations:
- Choose System assigned identity for Identity type under Managed identity.
- Select Identity-based access for Storage account access type under Storage account access.
- Under Data impact, check the High business impact workspace setting checkbox to enable the High Business Impact (HBI) feature for your new workspace.
- Choose Next : Tags to continue the setup.
- For Tags, use the Name and Value fields to create tags that will help organize the identity of the workspace. Choose Next : Review + create to validate the workspace setup.
- For Review + create, review the resource configuration details, then choose Create to create your new Azure Machine Learning workspace.
- For Basics, provide the following information:
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Repeat step no. 4 for each workspace that you want to re-create, available within the selected Azure subscription.
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Repeat steps no. 3 – 5 for each subscription available in your Microsoft Azure cloud account.
Using Azure CLI
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Define the configuration file for your new Azure Machine Learning workspace. Create a new configuration file (YAML format), name the file workspace-config.yml, and paste the following content. Set hbi_workspace to true to enable High Business Impact (HBI), and replace other configuration parameter values with your own information):
$schema: https://azuremlschemas.azureedge.net/latest/workspace.schema.json name: tm-new-project5-workspace location: westeurope display_name: tm-new-project5-workspace customer_managed_key: key_vault: /subscriptions/abcd1234-abcd-1234-abcd-1234abcd1234/resourceGroups/cloud-shell-storage-westeurope/providers/Microsoft.KeyVault/vaults/cc-project5-vault key_uri: https://cc-project5-vault.vault.azure.net/keys/cc-ml-workspace-key/abcd1234abcd1234abcd1234abcd1234 hbi_workspace: true
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Run ml workspace create command (Windows/macOS/Linux) with the configuration file defined at the previous step (i.e. workspace-config.yml), to create your new, HBI-protected Azure Machine Learning workspace:
az ml workspace create --resource-group cloud-shell-storage-westeurope --file workspace-config.yml
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The command output should return the new workspace configuration information:
{ "hbi_workspace": true, "customer_managed_key": { "cosmosdb_id": "", "key_uri": "https://cc-project5-vault.vault.azure.net/keys/cc-ml-workspace-key/abcd1234abcd1234abcd1234abcd1234", "key_vault": "/subscriptions/abcd1234-abcd-1234-abcd-1234abcd1234/resourceGroups/cloud-shell-storage-westeurope/providers/Microsoft.KeyVault/vaults/cc-project5-vault", "search_id": "", "storage_id": "" }, "description": "tm-new-project5-workspace", "discovery_url": "https://westeurope.api.azureml.ms/discovery", "display_name": "tm-new-project5-workspace", "enable_data_isolation": false, "id": "/subscriptions/abcd1234-abcd-1234-abcd-1234abcd1234/resourceGroups/cloud-shell-storage-westeurope/providers/Microsoft.MachineLearningServices/workspaces/tm-new-project5-workspace", "identity": { "principal_id": "abcd1234-abcd-1234-abcd-1234abcd1234", "tenant_id": "abcd1234-abcd-1234-abcd-1234abcd1234", "type": "system_assigned" }, "key_vault": "/subscriptions/abcd1234-abcd-1234-abcd-1234abcd1234/resourceGroups/cloud-shell-storage-westeurope/providers/Microsoft.Keyvault/vaults/1234abcd12341234abcd1234", "location": "westeurope", "managed_network": { "isolation_mode": "disabled", "outbound_rules": [] }, "mlflow_tracking_uri": "azureml://westeurope.api.azureml.ms/mlflow/v1.0/subscriptions/abcd1234-abcd-1234-abcd-1234abcd1234/resourceGroups/cloud-shell-storage-westeurope/providers/Microsoft.MachineLearningServices/workspaces/tm-new-project5-workspace", "name": "tm-new-project5-workspace", "public_network_access": "Enabled", "resourceGroup": "cloud-shell-storage-westeurope", "resource_group": "cloud-shell-storage-westeurope", "serverless_compute": { "no_public_ip": false }, "storage_account": "/subscriptions/abcd1234-abcd-1234-abcd-1234abcd1234/resourceGroups/cloud-shell-storage-westeurope/providers/Microsoft.Storage/storageAccounts/1234abcd12341234abcd1234", "tags": { "AttachAppInsightsToWorkspace": "false", "createdByToolkit": "cli-v2-1.16.1" } } -
Repeat steps no. 1 - 3 for each workspace that you want to re-create, available in the selected Azure subscription.
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Repeat steps no. 1 – 4 for each subscription available in your Microsoft Azure cloud account.
References
- Azure Official Documentation
- Data encryption with Azure Machine Learning
- Monitor Azure Machine Learning
- Manage Azure Machine Learning workspaces using Azure CLI
- Manage Azure Machine Learning workspaces in the portal or with the Python SDK (v2)
- Azure Command Line Interface (CLI) Documentation
- az ml workspace list
- az ml workspace show
- az ml workspace create
Related MachineLearning rules
- Enable Network Isolation for Azure Machine Learning Registries (Security)
- Check for Appropriate Admin SSH Public Key Management (Security)
- Check for Non-Standard SSH Port Configuration (Security)
- Use System-Assigned Managed Identities for Azure Machine Learning Workspaces (Security, operational-excellence)