Azure Policy Regulatory Compliance controls for Azure Machine Learning
Regulatory Compliance in Azure Policy provides Microsoft created and managed initiative definitions, known as built-ins, for the compliance domains and security controls related to different compliance standards. This page lists the compliance domains and security controls for Azure Machine Learning. You can assign the built-ins for a security control individually to help make your Azure resources compliant with the specific standard.
The title of each built-in policy definition links to the policy definition in the Azure portal. Use the link in the Policy Version column to view the source on the Azure Policy GitHub repo.
Important
Each control is associated with one or more Azure Policy definitions. These policies might help you assess compliance with the control. However, there often isn't a one-to-one or complete match between a control and one or more policies. As such, Compliant in Azure Policy refers only to the policies themselves. This doesn't ensure that you're fully compliant with all requirements of a control. In addition, the compliance standard includes controls that aren't addressed by any Azure Policy definitions at this time. Therefore, compliance in Azure Policy is only a partial view of your overall compliance status. The associations between controls and Azure Policy Regulatory Compliance definitions for these compliance standards can change over time.
FedRAMP High
To review how the available Azure Policy built-ins for all Azure services map to this compliance standard, see Azure Policy Regulatory Compliance - FedRAMP High. For more information about this compliance standard, see FedRAMP High.
| Domain | Control ID | Control title | Policy (Azure portal) |
Policy version (GitHub) |
|---|---|---|---|---|
| Access Control | AC-4 | Information Flow Enforcement | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | AC-17 | Remote Access | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | AC-17 (1) | Automated Monitoring / Control | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System And Communications Protection | SC-7 | Boundary Protection | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System And Communications Protection | SC-7 (3) | Access Points | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System And Communications Protection | SC-12 | Cryptographic Key Establishment And Management | Azure Machine Learning workspaces should be encrypted with a customer-managed key | 1.1.0 |
FedRAMP Moderate
To review how the available Azure Policy built-ins for all Azure services map to this compliance standard, see Azure Policy Regulatory Compliance - FedRAMP Moderate. For more information about this compliance standard, see FedRAMP Moderate.
| Domain | Control ID | Control title | Policy (Azure portal) |
Policy version (GitHub) |
|---|---|---|---|---|
| Access Control | AC-4 | Information Flow Enforcement | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | AC-17 | Remote Access | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | AC-17 (1) | Automated Monitoring / Control | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System And Communications Protection | SC-7 | Boundary Protection | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System And Communications Protection | SC-7 (3) | Access Points | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System And Communications Protection | SC-12 | Cryptographic Key Establishment And Management | Azure Machine Learning workspaces should be encrypted with a customer-managed key | 1.1.0 |
Microsoft cloud security benchmark
The Microsoft cloud security benchmark provides recommendations on how you can secure your cloud solutions on Azure. To see how this service completely maps to the Microsoft cloud security benchmark, see the Azure Security Benchmark mapping files.
To review how the available Azure Policy built-ins for all Azure services map to this compliance standard, see Azure Policy Regulatory Compliance - Microsoft cloud security benchmark.
| Domain | Control ID | Control title | Policy (Azure portal) |
Policy version (GitHub) |
|---|---|---|---|---|
| Network Security | NS-2 | Secure cloud services with network controls | Azure Machine Learning Computes should be in a virtual network | 1.0.1 |
| Network Security | NS-2 | Secure cloud services with network controls | Azure Machine Learning Workspaces should disable public network access | 2.0.1 |
| Network Security | NS-2 | Secure cloud services with network controls | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Identity Management | IM-1 | Use centralized identity and authentication system | Azure Machine Learning Computes should have local authentication methods disabled | 2.1.0 |
| Data Protection | DP-5 | Use customer-managed key option in data at rest encryption when required | Azure Machine Learning workspaces should be encrypted with a customer-managed key | 1.1.0 |
| Logging and Threat Detection | LT-3 | Enable logging for security investigation | Resource logs in Azure Machine Learning Workspaces should be enabled | 1.0.1 |
| Posture and Vulnerability Management | PV-2 | Audit and enforce secure configurations | Azure Machine Learning compute instances should be recreated to get the latest software updates | 1.0.3 |
NIST SP 800-171 R2
To review how the available Azure Policy built-ins for all Azure services map to this compliance standard, see Azure Policy Regulatory Compliance - NIST SP 800-171 R2. For more information about this compliance standard, see NIST SP 800-171 R2.
| Domain | Control ID | Control title | Policy (Azure portal) |
Policy version (GitHub) |
|---|---|---|---|---|
| Access Control | 3.1.1 | Limit system access to authorized users, processes acting on behalf of authorized users, and devices (including other systems). | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | 3.1.12 | Monitor and control remote access sessions. | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | 3.1.13 | Employ cryptographic mechanisms to protect the confidentiality of remote access sessions. | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | 3.1.14 | Route remote access via managed access control points. | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | 3.1.3 | Control the flow of CUI in accordance with approved authorizations. | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System and Communications Protection | 3.13.1 | Monitor, control, and protect communications (i.e., information transmitted or received by organizational systems) at the external boundaries and key internal boundaries of organizational systems. | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System and Communications Protection | 3.13.10 | Establish and manage cryptographic keys for cryptography employed in organizational systems. | Azure Machine Learning workspaces should be encrypted with a customer-managed key | 1.1.0 |
| System and Communications Protection | 3.13.2 | Employ architectural designs, software development techniques, and systems engineering principles that promote effective information security within organizational systems. | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System and Communications Protection | 3.13.5 | Implement subnetworks for publicly accessible system components that are physically or logically separated from internal networks. | Azure Machine Learning workspaces should use private link | 1.0.0 |
NIST SP 800-53 Rev. 4
To review how the available Azure Policy built-ins for all Azure services map to this compliance standard, see Azure Policy Regulatory Compliance - NIST SP 800-53 Rev. 4. For more information about this compliance standard, see NIST SP 800-53 Rev. 4.
| Domain | Control ID | Control title | Policy (Azure portal) |
Policy version (GitHub) |
|---|---|---|---|---|
| Access Control | AC-4 | Information Flow Enforcement | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | AC-17 | Remote Access | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | AC-17 (1) | Automated Monitoring / Control | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System And Communications Protection | SC-7 | Boundary Protection | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System And Communications Protection | SC-7 (3) | Access Points | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System And Communications Protection | SC-12 | Cryptographic Key Establishment And Management | Azure Machine Learning workspaces should be encrypted with a customer-managed key | 1.1.0 |
NIST SP 800-53 Rev. 5
To review how the available Azure Policy built-ins for all Azure services map to this compliance standard, see Azure Policy Regulatory Compliance - NIST SP 800-53 Rev. 5. For more information about this compliance standard, see NIST SP 800-53 Rev. 5.
| Domain | Control ID | Control title | Policy (Azure portal) |
Policy version (GitHub) |
|---|---|---|---|---|
| Access Control | AC-4 | Information Flow Enforcement | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | AC-17 | Remote Access | Azure Machine Learning workspaces should use private link | 1.0.0 |
| Access Control | AC-17 (1) | Monitoring and Control | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System and Communications Protection | SC-7 | Boundary Protection | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System and Communications Protection | SC-7 (3) | Access Points | Azure Machine Learning workspaces should use private link | 1.0.0 |
| System and Communications Protection | SC-12 | Cryptographic Key Establishment and Management | Azure Machine Learning workspaces should be encrypted with a customer-managed key | 1.1.0 |
NL BIO Cloud Theme
To review how the available Azure Policy built-ins for all Azure services map to this compliance standard, see Azure Policy Regulatory Compliance details for NL BIO Cloud Theme. For more information about this compliance standard, see Baseline Information Security Government Cybersecurity - Digital Government (digitaleoverheid.nl).
| Domain | Control ID | Control title | Policy (Azure portal) |
Policy version (GitHub) |
|---|---|---|---|---|
| C.04.6 Technical vulnerability management - Timelines | C.04.6 | Technical weaknesses can be remedied by performing patch management in a timely manner. | Azure Machine Learning compute instances should be recreated to get the latest software updates | 1.0.3 |
| U.05.2 Data protection - Cryptographic measures | U.05.2 | Data stored in the cloud service shall be protected to the latest state of the art. | Azure Machine Learning workspaces should be encrypted with a customer-managed key | 1.1.0 |
| U.07.1 Data separation - Isolated | U.07.1 | Permanent isolation of data is a multi-tenant architecture. Patches are realized in a controlled manner. | Azure Machine Learning Computes should be in a virtual network | 1.0.1 |
| U.07.1 Data separation - Isolated | U.07.1 | Permanent isolation of data is a multi-tenant architecture. Patches are realized in a controlled manner. | Azure Machine Learning Workspaces should disable public network access | 2.0.1 |
| U.07.1 Data separation - Isolated | U.07.1 | Permanent isolation of data is a multi-tenant architecture. Patches are realized in a controlled manner. | Azure Machine Learning workspaces should use private link | 1.0.0 |
| U.10.2 Access to IT services and data - Users | U.10.2 | Under the responsibility of the CSP, access is granted to administrators. | Azure Machine Learning Computes should have local authentication methods disabled | 2.1.0 |
| U.10.3 Access to IT services and data - Users | U.10.3 | Only users with authenticated equipment can access IT services and data. | Azure Machine Learning Computes should have local authentication methods disabled | 2.1.0 |
| U.10.5 Access to IT services and data - Competent | U.10.5 | Access to IT services and data is limited by technical measures and has been implemented. | Azure Machine Learning Computes should have local authentication methods disabled | 2.1.0 |
| U.11.3 Cryptoservices - Encrypted | U.11.3 | Sensitive data is always encrypted, with private keys managed by the CSC. | Azure Machine Learning workspaces should be encrypted with a customer-managed key | 1.1.0 |
| U.15.1 Logging and monitoring - Events logged | U.15.1 | The violation of the policy rules is recorded by the CSP and the CSC. | Resource logs in Azure Machine Learning Workspaces should be enabled | 1.0.1 |
Reserve Bank of India IT Framework for Banks v2016
To review how the available Azure Policy built-ins for all Azure services map to this compliance standard, see Azure Policy Regulatory Compliance - RBI ITF Banks v2016. For more information about this compliance standard, see RBI ITF Banks v2016 (PDF).
| Domain | Control ID | Control title | Policy (Azure portal) |
Policy version (GitHub) |
|---|---|---|---|---|
| Advanced Real-Timethreat Defenceand Management | Advanced Real-Timethreat Defenceand Management-13.4 | Azure Machine Learning workspaces should be encrypted with a customer-managed key | 1.1.0 | |
| Patch/Vulnerability & Change Management | Patch/Vulnerability & Change Management-7.7 | Azure Machine Learning workspaces should use private link | 1.0.0 |
Spain ENS
To review how the available Azure Policy built-ins for all Azure services map to this compliance standard, see Azure Policy Regulatory Compliance details for Spain ENS. For more information about this compliance standard, see CCN-STIC 884.
| Domain | Control ID | Control title | Policy (Azure portal) |
Policy version (GitHub) |
|---|---|---|---|---|
| Operational framework | op.exp.2 | Operation | Azure Machine Learning compute instances should be recreated to get the latest software updates | 1.0.3 |
| Operational framework | op.exp.3 | Operation | Azure Machine Learning compute instances should be recreated to get the latest software updates | 1.0.3 |
| Operational framework | op.exp.7 | Operation | Resource logs in Azure Machine Learning Workspaces should be enabled | 1.0.1 |
System and Organization Controls (SOC) 2
To review how the available Azure Policy built-ins for all Azure services map to this compliance standard, see Azure Policy Regulatory Compliance details for System and Organization Controls (SOC) 2. For more information about this compliance standard, see System and Organization Controls (SOC) 2.
| Domain | Control ID | Control title | Policy (Azure portal) |
Policy version (GitHub) |
|---|---|---|---|---|
| Logical and Physical Access Controls | CC6.1 | Logical access security software, infrastructure, and architectures | Azure Machine Learning workspaces should be encrypted with a customer-managed key | 1.1.0 |
Next steps
- Learn more about Azure Policy Regulatory Compliance.
- See the built-ins on the Azure Policy GitHub repo.