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Azure Machine Learning has several inbound and outbound dependencies. Some of these dependencies can expose a data exfiltration risk by malicious agents within your organization. This document explains how to minimize data exfiltration risk by limiting inbound and outbound requirements.
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*__Inbound__: Azure Machine Learning compute instance and compute cluster have two inbound requirements: the `batchnodemanagement` (ports 29876-29877) and `azuremachinelearning` (port 44224) service tags. You can control this inbound traffic by using a network security group (NSG) and service tags. It's difficult to disguise Azure service IPs, so there's low data exfiltration risk. You can also configure the compute to not use a public IP, which removes inbound requirements.
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*__Inbound__: If your compute instance or cluster uses a public IP address, you have an inbound on `azuremachinelearning` (port 44224) service tag. You can control this inbound traffic by using a network security group (NSG) and service tags. It's difficult to disguise Azure service IPs, so there's low data exfiltration risk. You can also configure the compute to not use a public IP, which removes inbound requirements.
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*__Outbound__: If malicious agents don't have write access to outbound destination resources, they can't use that outbound for data exfiltration. Azure Active Directory, Azure Resource Manager, Azure Machine Learning, and Microsoft Container Registry belong to this category. On the other hand, Storage and AzureFrontDoor.frontend can be used for data exfiltration.
>In the above text, `{region}` refers to the region code (for example, **eus** for East US and **ne** for North Europe). Refer to the following lists for regions codes:
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