Data Lake Storage Gen2 access denied for ML training jobs using service principal authentication

Cloud Platforms Microsoft Azure
, , , , , , , ,
1

Our Azure ML training jobs are failing with access denied errors when trying to read training data from Data Lake Storage Gen2. We’re using a service principal for authentication, and I’ve verified the credentials are correct because the same service principal can access the storage account from my local development environment using Azure CLI. The error occurs specifically when the ML compute cluster tries to access the data during job execution. Here’s the error we’re seeing:


Azure.Storage.DataLake.DataLakeServiceException:
Status: 403 (Forbidden)
ErrorCode: AuthorizationPermissionMismatch
Path: /training-data/invoices/dataset_v2.parquet

The service principal has been added to the storage account, but I’m not sure if the RBAC role assignment is correct or if there’s something specific about Data Lake Gen2 access control that I’m missing. The training data is in a container called ‘training-data’ and we need read access to multiple subdirectories. This is blocking our ML pipeline and we can’t proceed with model training until resolved.

2

To assign roles at container level, navigate to the specific container in Azure Portal (Storage account > Containers > select your container), then click ‘Access Control (IAM)’ on that container’s blade. From there you can add role assignments scoped to just that container. You’ll need to do this separately for both ‘training-data’ and ‘validation-data’ containers. Alternatively, you can use Azure CLI or PowerShell to script the role assignments which is easier if you have many containers to configure.

3

That makes sense about management vs data plane roles. How do I assign the role at container level? When I go to the storage account IAM blade, I can only select the storage account as the scope. Is there a different place to assign container-level roles?

4

I assigned ‘Contributor’ role at the storage account level thinking that would cover everything. Do I specifically need ‘Storage Blob Data Reader’ instead? And should it be at the storage account level or the container level? We have multiple containers in this storage account and the ML jobs need access to two of them - ‘training-data’ and ‘validation-data’.

5

‘Contributor’ is a management plane role that lets you configure the storage account but doesn’t grant data plane access to read/write blobs. You need data plane roles like ‘Storage Blob Data Reader’ for read access or ‘Storage Blob Data Contributor’ for read/write. For your use case with two containers, you have two options: assign the role at storage account level for access to all containers, or assign it at each container level for more granular security. I’d recommend container-level assignments following least privilege principle, especially if other containers in the same storage account contain sensitive data.

6

Let me provide a complete solution for your Data Lake Storage Gen2 access issue with Azure ML training jobs. The problem stems from incorrect RBAC role assignment and possibly datastore configuration.

Service Principal Authentication Setup: First, ensure your service principal is properly registered and has credentials (either certificate or client secret) that haven’t expired. You mentioned it works from your local environment, so the credentials are valid. The issue is specifically with how the service principal is authorized to access Data Lake Gen2 from the Azure ML compute cluster.

RBAC Role Assignment (Critical Fix): The ‘Contributor’ role you assigned is a management plane role that allows configuration changes to the storage account but does NOT grant data access. You need data plane roles. For read-only access to training data, assign ‘Storage Blob Data Reader’ role to your service principal. Here’s how:


az role assignment create \
  --role "Storage Blob Data Reader" \
  --assignee <service-principal-object-id> \
  --scope /subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.Storage/storageAccounts/<storage-account>/blobServices/default/containers/training-data

Repeat this for the ‘validation-data’ container. Container-level scope provides least privilege access. If you need access to all containers, use storage account scope instead (remove everything after ‘storageAccounts/’). Role propagation takes 5-10 minutes.

Data Lake Storage Gen2 Specific Considerations: Gen2 has hierarchical namespace enabled which means it supports both RBAC and POSIX-like ACLs. RBAC is the recommended approach and takes precedence. Ensure you haven’t set restrictive ACLs on the directories that might conflict with RBAC permissions. If ACLs are set, the service principal needs Execute permission on all parent directories and Read permission on the target files.

Azure ML Datastore Configuration: Verify your datastore is correctly configured to use the service principal. In Azure ML Studio, go to Datastores, select your Gen2 datastore, and confirm the authentication method is set to ‘Service Principal’ with the correct tenant ID, client ID, and client secret. The datastore configuration must reference the same service principal that has the RBAC role assigned. If you created the datastore with different credentials, recreate it or update the credentials.

Testing Access: After role assignment propagates, test access by running a simple script in an Azure ML notebook using the compute cluster:

from azure.identity import ClientSecretCredential
from azure.storage.filedatalake import DataLakeServiceClient

credential = ClientSecretCredential(tenant_id, client_id, client_secret)
service_client = DataLakeServiceClient(account_url, credential=credential)
file_system = service_client.get_file_system_client("training-data")
print(file_system.get_paths())

If this works but your training job still fails, the issue is in how the training script or Azure ML pipeline is referencing the datastore. Ensure you’re using the registered datastore reference, not hardcoded storage URLs.

Common Pitfalls: 1) Using workspace managed identity instead of service principal - check which identity your training job actually uses. 2) Role assigned to wrong object (user instead of service principal). 3) Not waiting for role propagation. 4) Firewall rules blocking compute cluster - if storage account has network restrictions, add the Azure ML compute subnet to allowed networks. 5) Expired service principal credentials.

After implementing these fixes, your ML training jobs should successfully read from Data Lake Gen2. The 403 error will resolve once the service principal has proper data plane permissions through RBAC role assignment.