Ovaj preglednik više nije podržan.
Prijeđite na Microsoft Edge, gdje vas čekaju najnovije značajke, sigurnosna ažuriranja i tehnička podrška.
This security baseline applies guidance from the Microsoft cloud security benchmark version 1.0 to Machine Learning Service. The Microsoft cloud security benchmark provides recommendations on how you can secure your cloud solutions on Azure. The content is grouped by the security controls defined by the Microsoft cloud security benchmark and the related guidance applicable to Machine Learning Service.
You can monitor this security baseline and its recommendations using Microsoft Defender for Cloud. Azure Policy definitions will be listed in the Regulatory Compliance section of the Microsoft Defender for Cloud portal page.
When a feature has relevant Azure Policy Definitions, they are listed in this baseline to help you measure compliance with the Microsoft cloud security benchmark controls and recommendations. Some recommendations may require a paid Microsoft Defender plan to enable certain security scenarios.
Napomena
Features not applicable to Machine Learning Service have been excluded. To see how Machine Learning Service completely maps to the Microsoft cloud security benchmark, see the full Machine Learning Service security baseline mapping file.
The security profile summarizes high-impact behaviors of Machine Learning Service, which may result in increased security considerations.
| Service Behavior Attribute | Value |
|---|---|
| Product Category | AI+ML |
| Customer can access HOST / OS | Full Access |
| Service can be deployed into customer's virtual network | True |
| Stores customer content at rest | False |
For more information, see the Microsoft cloud security benchmark: Network security.
Description: Service supports deployment into customer's private Virtual Network (VNet). Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Shared |
Configuration Guidance: Use managed network isolation to provide automated network isolation experience.
Note: You can also use your virtual network for Azure Machine Learning resources, but several computing types are not supported.
Reference: Secure Azure Machine Learning workspace resources using virtual networks (VNets)
Description: Service network traffic respects Network Security Groups rule assignment on its subnets. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Shared |
Configuration Guidance: Use managed network isolation to provide automated network isolation experience which includes inbound and outbound configurations using NSG.
Note: Use network security groups (NSG) to restrict or monitor traffic by port, protocol, source IP address, or destination IP address. Create NSG rules to restrict your service's open ports (such as preventing management ports from being accessed from untrusted networks). Be aware that by default, NSGs deny all inbound traffic but allow traffic from virtual network and Azure Load Balancers.
Reference: Plan for network isolation
Description: Service native IP filtering capability for filtering network traffic (not to be confused with NSG or Azure Firewall). Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Deploy private endpoints for all Azure resources that support the Private Link feature, to establish a private access point for the resources.
Reference: Configure a private endpoint for an Azure Machine Learning workspace
Description: Service supports disabling public network access either through using service-level IP ACL filtering rule (not NSG or Azure Firewall) or using a 'Disable Public Network Access' toggle switch. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Disable public network access either using the service-level IP ACL filtering rule or a toggling switch for public network access.
Reference: Configure a private endpoint for an Azure Machine Learning workspace
Azure Policy built-in definitions - Microsoft.MachineLearningServices:
| Name (Azure portal) |
Description | Effect(s) | Version (GitHub) |
|---|---|---|---|
| Azure Machine Learning Computes should be in a virtual network | Azure Virtual Networks provide enhanced security and isolation for your Azure Machine Learning Compute Clusters and Instances, as well as subnets, access control policies, and other features to further restrict access. When a compute is configured with a virtual network, it is not publicly addressable and can only be accessed from virtual machines and applications within the virtual network. | Audit, Disabled | 1.0.1 |
For more information, see the Microsoft cloud security benchmark: Identity management.
Description: Service supports using Azure AD authentication for data plane access. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | True | Microsoft |
Configuration Guidance: No additional configurations are required as this is enabled on a default deployment.
Reference: Set up authentication for Azure Machine Learning resources and workflows
Description: Local authentications methods supported for data plane access, such as a local username and password. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Configuration Guidance: This feature is not supported to secure this service.
Description: Data plane actions support authentication using managed identities. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Use Azure managed identities instead of service principals when possible, which can authenticate to Azure services and resources that support Azure Active Directory (Azure AD) authentication. Managed identity credentials are fully managed, rotated, and protected by the platform, avoiding hard-coded credentials in source code or configuration files.
Reference: Set up authentication between Azure Machine Learning and other services
Description: Data plane supports authentication using service principals. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: There is no current Microsoft guidance for this feature configuration. Please review and determine if your organization wants to configure this security feature.
Reference: Set up authentication between Azure Machine Learning and other services
Description: Data plane access can be controlled using Azure AD Conditional Access Policies. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Define the applicable conditions and criteria for Azure Active Directory (Azure AD) conditional access in the workload. Consider common use cases such as blocking or granting access from specific locations, blocking risky sign-in behavior, or requiring organization-managed devices for specific applications.
Reference: Use Conditional Access
Description: Data plane supports native use of Azure Key Vault for credential and secrets store. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Ensure that secrets and credentials are stored in secure locations such as Azure Key Vault, instead of embedding them into code or configuration files.
Reference: Use authentication credential secrets in Azure Machine Learning jobs
For more information, see the Microsoft cloud security benchmark: Privileged access.
Description: Service has the concept of a local administrative account. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Configuration Guidance: This feature is not supported to secure this service.
Description: Azure Role-Based Access Control (Azure RBAC) can be used to managed access to service's data plane actions. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Use Azure role-based access control (Azure RBAC) to manage Azure resource access through built-in role assignments. Azure RBAC roles can be assigned to users, groups, service principals, and managed identities.
Reference: Manage access to an Azure Machine Learning workspace
Description: Customer Lockbox can be used for Microsoft support access. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Configuration Guidance: This feature is not supported to secure this service.
For more information, see the Microsoft cloud security benchmark: Data protection.
Description: Tools (such as Azure Purview or Azure Information Protection) can be used for data discovery and classification in the service. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Use tools such as Azure Purview, Azure Information Protection, and Azure SQL Data Discovery and Classification to centrally scan, classify and label any sensitive data that resides in Azure, on-premises, Microsoft 365, or other locations.
Reference: Connect to and manage Azure Machine Learning in Microsoft Purview
Description: Service supports DLP solution to monitor sensitive data movement (in customer's content). Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: If required for compliance of data loss prevention (DLP), you can use a data exfiltration protection configuration. Managed network isolation also supports data exfiltration protection.
Reference: Azure Machine Learning data exfiltration prevention
Description: Service supports data in-transit encryption for data plane. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | True | Microsoft |
Feature notes: Azure Machine Learning uses TLS to secure internal communication between various Azure Machine Learning microservices. All Azure Storage access also occurs over a secure channel.
For information on how to secure a Kubernetes online endpoint that's created through Azure Machine Learning, please visit: Configure a secure online endpoint with TLS/SSL
Configuration Guidance: No additional configurations are required as this is enabled on a default deployment.
Reference: Encryption in transit
Description: Data at-rest encryption using platform keys is supported, any customer content at rest is encrypted with these Microsoft managed keys. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | True | Microsoft |
Configuration Guidance: No additional configurations are required as this is enabled on a default deployment.
Reference: Data encryption with Azure Machine Learning
Description: Data at-rest encryption using customer-managed keys is supported for customer content stored by the service. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: If required for regulatory compliance, define the use case and service scope where encryption using customer-managed keys are needed. Enable and implement data at rest encryption using customer-managed key for those services.
Reference: Customer-managed keys for Azure Machine Learning
Azure Policy built-in definitions - Microsoft.MachineLearningServices:
| Name (Azure portal) |
Description | Effect(s) | Version (GitHub) |
|---|---|---|---|
| Azure Machine Learning workspaces should be encrypted with a customer-managed key | Manage encryption at rest of Azure Machine Learning workspace data with customer-managed keys. By default, customer data is encrypted with service-managed keys, but customer-managed keys are commonly required to meet regulatory compliance standards. Customer-managed keys enable the data to be encrypted with an Azure Key Vault key created and owned by you. You have full control and responsibility for the key lifecycle, including rotation and management. Learn more at https://aka.ms/azureml-workspaces-cmk. | Audit, Deny, Disabled | 1.0.3 |
Description: The service supports Azure Key Vault integration for any customer keys, secrets, or certificates. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Use Azure Key Vault to create and control the life cycle of your encryption keys, including key generation, distribution, and storage. Rotate and revoke your keys in Azure Key Vault and your service based on a defined schedule or when there is a key retirement or compromise. When there is a need to use customer-managed key (CMK) in the workload, service, or application level, ensure you follow the best practices for key management: Use a key hierarchy to generate a separate data encryption key (DEK) with your key encryption key (KEK) in your key vault. Ensure keys are registered with Azure Key Vault and referenced via key IDs from the service or application. If you need to bring your own key (BYOK) to the service (such as importing HSM-protected keys from your on-premises HSMs into Azure Key Vault), follow recommended guidelines to perform initial key generation and key transfer.
Reference: Customer-managed keys for Azure Machine Learning
Description: The service supports Azure Key Vault integration for any customer certificates. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Configuration Guidance: This feature is not supported to secure this service.
For more information, see the Microsoft cloud security benchmark: Asset management.
Description: Service configurations can be monitored and enforced via Azure Policy. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Use Microsoft Defender for Cloud to configure Azure Policy to audit and enforce configurations of your Azure resources. Use Azure Monitor to create alerts when there is a configuration deviation detected on the resources. Use Azure Policy [deny] and [deploy if not exists] effects to enforce secure configuration across Azure resources.
Reference: Azure Policy built-in policy definitions for Azure Machine Learning
Description: Service can limit what customer applications run on the virtual machine using Adaptive Application Controls in Microsoft Defender for Cloud. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Configuration Guidance: This feature is not supported to secure this service.
For more information, see the Microsoft cloud security benchmark: Logging and threat detection.
Description: Service has an offering-specific Microsoft Defender solution to monitor and alert on security issues. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Feature notes: If using your own custom containers or clusters for Azure Machine Learning, you should enable scanning of your Azure Container Registry resource and Azure Kubernetes Service resources through Microsoft Defender for Cloud. However, Microsoft Defender for Cloud cannot be used on Azure Machine Learning managed compute instances or compute clusters.
Configuration Guidance: This feature is not supported to secure this service.
Description: Service produces resource logs that can provide enhanced service-specific metrics and logging. The customer can configure these resource logs and send them to their own data sink like a storage account or log analytics workspace. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Enable resource logs for the service. For example, Key Vault supports additional resource logs for actions that get a secret from a key vault or and Azure SQL has resource logs that track requests to a database. The content of resource logs varies by the Azure service and resource type.
Reference: Monitor Azure Machine Learning
For more information, see the Microsoft cloud security benchmark: Posture and vulnerability management.
Description: Azure Automation State Configuration can be used to maintain the security configuration of the operating system. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Configuration Guidance: This feature is not supported to secure this service.
Description: Azure Policy guest configuration agent can be installed or deployed as an extension to compute resources. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Use Microsoft Defender for Cloud and Azure Policy guest configuration agent to regularly assess and remediate configuration deviations on your Azure compute resources, including VMs, containers, and others.
Description: Service supports using user-supplied VM images or pre-built images from the marketplace with certain baseline configurations pre-applied. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Configuration Guidance: This feature is not supported to secure this service.
Description: Service supports using user-supplied container images or pre-built images from the marketplace with certain baseline configurations pre-applied. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: Use a pre-configured hardened image from a trusted supplier such as Microsoft or build the desired secure configuration baseline into the container image template
Reference: Train a model by using a custom Docker image
Description: Service can be scanned for vulnerability scan using Microsoft Defender for Cloud or other Microsoft Defender services embedded vulnerability assessment capability (including Microsoft Defender for server, container registry, App Service, SQL, and DNS). Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Feature notes: Defender for Server agent installation is currently not supported, however Trivy may be installed on the compute instances to discover OS and Python package level vulnerabilities.
For more information, please visit: Vulnerability management for Azure Machine Learning
Configuration Guidance: This feature is not supported to secure this service.
Description: Service can use Azure Automation Update Management to deploy patches and updates automatically. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Feature notes: Compute clusters automatically upgrade to the latest VM image. If the cluster is configured with min nodes = 0, it automatically upgrades nodes to the latest VM image version when all jobs are completed and the cluster reduces to zero nodes.
Compute instances get the latest VM images at the time of provisioning. Microsoft releases new VM images on a monthly basis. Once a compute instance is deployed, it does not get actively updated. To keep current with the latest software updates and security patches, you could:
Recreate a compute instance to get the latest OS image (recommended)
Alternatively, regularly update OS and Python packages.
Configuration Guidance: This feature is not supported to secure this service.
For more information, see the Microsoft cloud security benchmark: Endpoint security.
Description: Endpoint Detection and Response (EDR) feature such as Azure Defender for servers can be deployed into the endpoint. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Configuration Guidance: This feature is not supported to secure this service.
Description: Anti-malware feature such as Microsoft Defender Antivirus, Microsoft Defender for Endpoint can be deployed on the endpoint. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: ClamAV may be used to discover malware and comes pre-installed on compute instance.
Reference: Vulnerability management on compute hosts
Description: Anti-malware solution provides health status monitoring for platform, engine, and automatic signature updates. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| True | False | Customer |
Configuration Guidance: ClamAV may be used to discover malware and comes pre-installed on compute instance.
For more information, see the Microsoft cloud security benchmark: Backup and recovery.
Description: The service can be backed up by the Azure Backup service. Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Configuration Guidance: This feature is not supported to secure this service.
Description: Service supports its own native backup capability (if not using Azure Backup). Learn more.
| Supported | Enabled By Default | Configuration Responsibility |
|---|---|---|
| False | Not Applicable | Not Applicable |
Configuration Guidance: This feature is not supported to secure this service.
Obuka
Modul
Protect your virtual machines by using Azure Backup - Training
Use Azure Backup to help protect on-premises servers, virtual machines, SQL Server, Azure file shares, and other workloads.
Certifikacija
Microsoft Certified: Information Protection and Compliance Administrator Associate - Certifications
Demonstrate the fundamentals of data security, lifecycle management, information security, and compliance to protect a Microsoft 365 deployment.
Dokumentacija
Enterprise security and governance - Azure Machine Learning
Securely use Azure Machine Learning: authentication, authorization, network security, data encryption, and monitoring.
Machine learning enterprise security - Cloud Adoption Framework
This article provides best practices when planning or managing a secure Azure Machine Learning deployment.
Compare network isolation configurations - Azure Machine Learning
Azure Machine Learning can use both managed and custom virtual networks for network isolation. Learn about the differences between the two configurations.