本页面介绍了 Google Cloud 中 v1.0 版本的预定义安全 AI 状况。此安全状况 包含两个政策集：
一组政策，其中包含应用于 Vertex AI 工作负载。
一组政策，其中包含适用于 Vertex AI 工作负载。
您可以使用这一预定义的安全状况 配置安全状况，帮助保护 Gemini 和 Vertex AI 资源。如果您想部署此预定义状况，则必须 自定义一些政策，以使其适用于您的环境。
组织政策限制条件
下表介绍了 这种安全状况。
| 政策 | 说明 | 合规性标准 |
|---|---|---|
ainotebooks.accessMode |
此限制条件 定义了 Vertex AI Workbench 允许的访问模式 笔记本和实例。 采用时，您必须配置此值 安全状况 |
NIST SP 800-53 对照组：AC-3(3) 和 AC-6(1) |
ainotebooks.disableFileDownloads |
此限制条件 阻止使用该文件创建 Vertex AI Workbench 实例 下载选项已启用。默认情况下，您可在以下位置启用文件下载选项： 任何 Vertex AI Workbench 实例。 该值介于 |
NIST SP 800-53 对照组：AC-3(1) |
ainotebooks.disableRootAccess |
此限制条件可防止 新创建的 Vertex AI Workbench 用户管理的笔记本和实例 无法启用 root 访问权限。默认情况下，Vertex AI Workbench 用户管理 可以启用 root 访问权限。 该值为
|
NIST SP 800-53 对照组：AC-3 和 AC-6(2) |
ainotebooks.disableTerminal |
此限制条件会阻止创建 Vertex AI Workbench 实例 终端。默认情况下，终端可以启用 Vertex AI Workbench 实例。 该值介于 |
NIST SP 800-53 控件：AC-3、AC-6 和 CM-2 |
ainotebooks.environmentOptions |
此限制条件定义了 用户可以在创建新映像时选择的虚拟机和容器映像选项 具有此限制条件的 Vertex AI Workbench 笔记本和实例 强制执行。必须明确列出要允许或拒绝的选项。 具体值如下：
policy_rules:
- values:
allowed_values:
- is:ainotebooks-vm/deeplearning-platform-release/image-family/tf-1-15-cpu
- is:ainotebooks-vm/deeplearning-platform-release/image-family/tf-2-1-cpu
- is:ainotebooks-vm/deeplearning-platform-release/image-family/tf-1-15-gpu
- is:ainotebooks-vm/deeplearning-platform-release/image-family/tf-2-1-gpu
- is:ainotebooks-vm/deeplearning-platform-release/image-family/caffe1-latest-cpu-experimental
- is:ainotebooks-vm/deeplearning-platform-release/image-name/r-3-6-cpu-experimental-20200617
- is:ainotebooks-vm/deeplearning-platform-release/image-name/tf2-ent-2-1-cpu-20200613
- is:ainotebooks-vm/deeplearning-platform-release/image-name/tf2-2-2-cu101-20200616
- is:ainotebooks-vm/deeplearning-platform-release/image-name/tf-1-15-cu100-20200615
- is:ainotebooks-vm/deeplearning-platform-release/image-name/pytorch-latest-cpu-20200615
- is:ainotebooks-container/gcr.io/deeplearning-platform-release/tf-gpu.1-15
- is:ainotebooks-container/gcr.io/deeplearning-platform-release/tf-cpu.1-15:latest
- is:ainotebooks-container/gcr.io/deeplearning-platform-release/tf-cpu.1-15:m48
- is:ainotebooks-container/gcr.io/deeplearning-platform-release/tf-cpu.1-15:m46
- is:ainotebooks-container/custom-container:latest
|
NIST SP 800-53 控件：AC-3、AC-6 和 CM-2 |
ainotebooks.requireAutoUpgradeSchedule |
此限制条件 要求新创建的 Vertex AI Workbench 用户管理的笔记本 具有自动升级时间表。 该值为
|
NIST SP 800-53 对照组：AU-9、CM-2 和 CM-6 |
ainotebooks.restrictPublicIp |
此限制条件会限制 允许访问新创建的 Vertex AI Workbench 笔记本的公共 IP 访问权限， 实例。默认情况下，公共 IP 可以访问 Vertex AI Workbench 笔记本 和实例 值为 |
NIST SP 800-53 对照组：AC-3、AC-4 和 SC-7 |
ainotebooks.restrictVpcNetworks |
此列表定义了 用户在创建新的 Vertex AI Workbench 时可以选择的 VPC 网络 会强制执行此限制条件的实例。 您必须配置此值 。 |
NIST SP 800-53 控件：AC-3、AC-4 和 CM-2 |
Security Health Analytics 检测器
下表介绍了 。
| 检测器名称 | 适用的资源 | 说明 | 合规性标准 |
|---|---|---|---|
| vertexAIDataSetCMEKDisabled | aiplatform.googleapis.com/Dataset |
此检测器会检查 任何数据集均未使用客户管理的加密密钥进行加密 (CMEK)。 如需解决此发现结果，请验证您是否创建了密钥并 创建密钥环时，设置权限，并在创建密钥时提供密钥 数据集。有关说明，请参阅为您的 资源。 |
NIST SP 800-53 对照组：SC12 和 SC13 |
| vertexAIModelCMEKDisabled | aiplatform.googleapis.com/Model |
此检测器会检查模型是否未使用 CMEK 进行加密。 如需解决此发现结果，请验证您是否创建了密钥并 创建密钥环时，设置权限，并在创建密钥时提供密钥 model.有关说明，请参阅为您的 资源。 |
NIST SP 800-53 对照组：SC12 和 SC13 |
| vertexAIEndpointCMEKDisabled | aiplatform.googleapis.com/Endpoint |
此检测器会检查端点是否未使用 CMEK 进行加密。 如需解决此发现结果，请验证您是否创建了密钥并 创建密钥环时，设置权限，并在创建密钥时提供密钥 端点。有关说明，请参阅为您的 资源。 |
NIST SP 800-53 对照组：SC12 和 SC13 |
| vertexAITrainingPipelineCMEKDisabled | aiplatform.googleapis.com/TrainingPipeline |
此检测器会检查训练流水线是否未使用 CMEK 进行加密。 如需解决此发现结果，请验证您是否创建了密钥并 创建密钥环时，设置权限，并在创建密钥时提供密钥 训练流水线。有关说明，请参阅为您的 资源。 |
NIST SP 800-53 对照组：SC12 和 SC13 |
| vertexAIDataLabelingJobCMEKDisabled | aiplatform.googleapis.com/DataLabelingJob |
此检测器会检查数据标签是否未使用 CMEK 进行加密。 如需解决此发现结果，请验证您是否创建了密钥并 创建密钥环时，设置权限，并在创建密钥时提供密钥 数据标签。有关说明，请参阅为您的 资源。 |
NIST SP 800-53 对照组：SC12 和 SC13 |
| vertexAICustomJobCMEKDisabled | aiplatform.googleapis.com/CustomJob |
此检测器会检查运行自定义工作负载的作业是否未使用 CMEK 进行加密。 如需解决此发现结果，请验证您是否创建了密钥并 创建密钥环时，设置权限，并在创建密钥时提供密钥 自定义作业。有关说明，请参阅为您的 资源。 |
NIST SP 800-53 对照组：SC12 和 SC13 |
| vertexAIDataLabelingJobHyperparameterTuningJobCMEKDisabled | aiplatform.googleapis.com/HyperparameterTuningJob |
此检测器会检查超参数调优作业是否未使用 CMEK 进行加密。 如需解决此发现结果，请验证您是否创建了密钥并 创建密钥环时，设置权限，并在创建密钥时提供密钥 超参数调优作业。有关说明，请参阅为您的 资源。 |
NIST SP 800-53 对照组：SC12 和 SC13 |
YAML 定义
以下是预定义安全 AI 状况的 YAML 定义。
name: organizations/123/locations/global/postureTemplates/secure_ai_extended
description: Posture Template to make your AI workload secure.
revision_id: v.1.0
state: ACTIVE
policy_sets:
- policy_set_id: Secure-AI policy_set
description: 8 org policies that new customers can automatically enable.
policies:
- policy_id: Define access mode for Vertex AI Workbench notebooks and instances
compliance_standards:
- standard: NIST SP 800-53
control: AC-3(3)
- standard: NIST SP 800-53
control: AC-6(1)
constraint:
org_policy_constraint:
canned_constraint_id: ainotebooks.accessMode
policy_rules:
- values:
allowed_values:
- is:service-account
- is:single-user
description: This list constraint defines the modes of access allowed to Vertex AI Workbench notebooks and instances where enforced. The allow or deny list can specify multiple users with the service-account mode or single-user access with the single-user mode. The access mode to be allowed or denied must be listed explicitly.
- policy_id: Disable file downloads on new Vertex AI Workbench instances
compliance_standards:
- standard: NIST SP 800-53
control: AC-3(1)
constraint:
org_policy_constraint:
canned_constraint_id: ainotebooks.disableFileDownloads
policy_rules:
- enforce: true
description: This boolean constraint, when enforced, prevents the creation of Vertex AI Workbench instances with the file download option enabled. By default, the file download option can be enabled on any Vertex AI Workbench instance.
- policy_id: Disable root access on new Vertex AI Workbench user-managed notebooks and instances
compliance_standards:
- standard: NIST SP 800-53
control: AC-3
- standard: NIST SP 800-53
control: AC-6(2)
constraint:
org_policy_constraint:
canned_constraint_id: ainotebooks.disableRootAccess
policy_rules:
- enforce: true
description: This boolean constraint, when enforced, prevents newly created Vertex AI Workbench user-managed notebooks and instances from enabling root access. By default, Vertex AI Workbench user-managed notebooks and instances can have root access enabled.
- policy_id: Disable terminal on new Vertex AI Workbench instances
compliance_standards:
- standard: NIST SP 800-53
control: AC-3
- standard: NIST SP 800-53
control: AC-6
- standard: NIST SP 800-53
control: CM-2
constraint:
org_policy_constraint:
canned_constraint_id: ainotebooks.disableTerminal
policy_rules:
- enforce: true
description: This boolean constraint, when enforced, prevents the creation of Vertex AI Workbench instances with the terminal enabled. By default, the terminal can be enabled on Vertex AI Workbench instances.
- policy_id: Restrict environment options on new Vertex AI Workbench notebooks and instances
compliance_standards:
- standard: NIST SP 800-53
control: AC-3
- standard: NIST SP 800-53
control: AC-6
- standard: NIST SP 800-53
control: CM-2
constraint:
org_policy_constraint:
canned_constraint_id: ainotebooks.environmentOptions
policy_rules:
- values:
allowed_values:
- is:ainotebooks-vm/deeplearning-platform-release/image-family/tf-1-15-cpu
- is:ainotebooks-vm/deeplearning-platform-release/image-family/tf-2-1-cpu
- is:ainotebooks-vm/deeplearning-platform-release/image-family/tf-1-15-gpu
- is:ainotebooks-vm/deeplearning-platform-release/image-family/tf-2-1-gpu
- is:ainotebooks-vm/deeplearning-platform-release/image-family/caffe1-latest-cpu-experimental
- is:ainotebooks-vm/deeplearning-platform-release/image-name/r-3-6-cpu-experimental-20200617
- is:ainotebooks-vm/deeplearning-platform-release/image-name/tf2-ent-2-1-cpu-20200613
- is:ainotebooks-vm/deeplearning-platform-release/image-name/tf2-2-2-cu101-20200616
- is:ainotebooks-vm/deeplearning-platform-release/image-name/tf-1-15-cu100-20200615
- is:ainotebooks-vm/deeplearning-platform-release/image-name/pytorch-latest-cpu-20200615
- is:ainotebooks-container/gcr.io/deeplearning-platform-release/tf-gpu.1-15
- is:ainotebooks-container/gcr.io/deeplearning-platform-release/tf-cpu.1-15:latest
- is:ainotebooks-container/gcr.io/deeplearning-platform-release/tf-cpu.1-15:m48
- is:ainotebooks-container/gcr.io/deeplearning-platform-release/tf-cpu.1-15:m46
- is:ainotebooks-container/custom-container:latest
description: "This list constraint defines the VM and container image options a user can select when creating new Vertex AI Workbench notebooks and instances where this constraint is enforced. The options to be allowed or denied must be listed explicitly. \n
The expected format for VM instances is ainotebooks-vm/PROJECT_ID/IMAGE_TYPE/CONSTRAINED_VALUE. Replace IMAGE_TYPE with image-family or image-name. Examples: ainotebooks-vm/deeplearning-platform-release/image-family/pytorch-1-4-cpu, ainotebooks-vm/deeplearning-platform-release/image-name/pytorch-latest-cpu-20200615. \n
The expected format for container images will be ainotebooks-container/CONTAINER_REPOSITORY:TAG. Examples: ainotebooks-container/gcr.io/deeplearning-platform-release/tf-gpu.1-15:latest, ainotebooks-container/gcr.io/deeplearning-platform-release/tf-gpu.1-15:m48."
- policy_id: Require automatic scheduled upgrades on new Vertex AI Workbench user-managed notebooks and instances
compliance_standards:
- standard: NIST SP 800-53
control: AU-9
- standard: NIST SP 800-53
control: CM-2
- standard: NIST SP 800-53
control: CM-6
constraint:
org_policy_constraint:
canned_constraint_id: ainotebooks.requireAutoUpgradeSchedule
policy_rules:
- enforce: true
description: This boolean constraint, when enforced, requires that newly created Vertex AI Workbench user-managed notebooks and instances have an automatic upgrade schedule set. The automatic upgrade schedule can be defined by using the `notebook-upgrade-schedule` metadata flag to specify a cron schedule for the automatic upgrades.
- policy_id: Restrict public IP access on new Vertex AI Workbench notebooks and instances
compliance_standards:
- standard: NIST SP 800-53
control: AC-3
- standard: NIST SP 800-53
control: AC-4
- standard: NIST SP 800-53
control: SC-7
constraint:
org_policy_constraint:
canned_constraint_id: ainotebooks.restrictPublicIp
policy_rules:
- enforce: true
description: This boolean constraint, when enforced, restricts public IP access to newly created Vertex AI Workbench notebooks and instances. By default, public IPs can access Vertex AI Workbench notebooks and instances.
- policy_id: Restrict VPC networks on new Vertex AI Workbench instances
compliance_standards:
- standard: NIST SP 800-53
control: AC-3
- standard: NIST SP 800-53
control: AC-4
- standard: NIST SP 800-53
control: CM-2
constraint:
org_policy_constraint:
canned_constraint_id: ainotebooks.restrictVpcNetworks
policy_rules:
- values:
allowed_values:
- is:organizations/ORGANIZATION_ID
- is:folders/FOLDER_ID
- is:projects/PROJECT_ID
- is:projects/PROJECT_ID/global/networks/NETWORK_NAME
description: This list constraint defines the VPC networks a user can select when creating new Vertex AI Workbench instances where this constraint is enforced. By default, a Vertex AI Workbench instance can be created with any VPC networks. The allowed or denied list of networks must be identified in the form.
- policy_set_id: Secure-AI SHA_policy_set
description: 5 custome SHA modules that new customers can automatically enable.
policies:
- policy_id: CMEK key is use for Vertex AI DataSet
compliance_standards:
- standard: NIST SP 800-53
control: SC-12
- standard: NIST SP 800-53
control: SC-13
constraint:
security_health_analytics_custom_module:
display_name: "vertexAIDataSetCMEKDisabled"
config:
customOutput: {}
predicate:
expression: "!has(resource.encryptionSpec)"
resource_selector:
resource_types:
- aiplatform.googleapis.com/Dataset
severity: CRITICAL
description: "When enforced, this detector finds if any Data Set is not encrypted using CMEK. CMEKs, managed via Cloud KMS, offer advanced control over key operations."
recommendation: "Restore SHA module- Reset the SHA module to its intended state. Consult documentation- Refer to the comprehensive guidance provided at
https://cloud.google.com/security-command-center/docs/custom-modules-sha-overview"
module_enablement_state: ENABLED
- policy_id: CMEK key is use for Vertex AI Model
compliance_standards:
- standard: NIST SP 800-53
control: SC-12
- standard: NIST SP 800-53
control: SC-13
constraint:
security_health_analytics_custom_module:
display_name: "vertexAIModelCMEKDisabled"
config:
customOutput: {}
predicate:
expression: "!has(resource.encryptionSpec)"
resource_selector:
resource_types:
- aiplatform.googleapis.com/Model
severity: CRITICAL
description: "When enforced, this detector finds if any Data Set is not encrypted using CMEK. CMEKs, managed via Cloud KMS, offer advanced control over key operations."
recommendation: "Restore SHA module- Reset the SHA module to its intended state. Consult documentation- Refer to the comprehensive guidance provided at
https://cloud.google.com/security-command-center/docs/custom-modules-sha-overview"
module_enablement_state: ENABLED
- policy_id: CMEK key is use for Vertex AI Endpoint
compliance_standards:
- standard: NIST SP 800-53
control: SC-12
- standard: NIST SP 800-53
control: SC-13
constraint:
security_health_analytics_custom_module:
display_name: "vertexAIEndpointCMEKDisabled"
config:
customOutput: {}
predicate:
expression: "!has(resource.encryptionSpec)"
resource_selector:
resource_types:
- aiplatform.googleapis.com/Endpoint
severity: CRITICAL
description: "When enforced, this detector finds if any Data Set is not encrypted using CMEK. CMEKs, managed via Cloud KMS, offer advanced control over key operations."
recommendation: "Restore SHA module- Reset the SHA module to its intended state. Consult documentation- Refer to the comprehensive guidance provided at
https://cloud.google.com/security-command-center/docs/custom-modules-sha-overview"
module_enablement_state: ENABLED
- policy_id: CMEK key is use for Vertex AI TrainingPipeline
compliance_standards:
- standard: NIST SP 800-53
control: SC-12
- standard: NIST SP 800-53
control: SC-13
constraint:
security_health_analytics_custom_module:
display_name: "vertexAITrainingPipelineCMEKDisabled"
config:
customOutput: {}
predicate:
expression: "!has(resource.encryptionSpec)"
resource_selector:
resource_types:
- aiplatform.googleapis.com/TrainingPipeline
severity: CRITICAL
description: "When enforced, this detector finds if any Data Set is not encrypted using CMEK. CMEKs, managed via Cloud KMS, offer advanced control over key operations."
recommendation: "Restore SHA module- Reset the SHA module to its intended state. Consult documentation- Refer to the comprehensive guidance provided at
https://cloud.google.com/security-command-center/docs/custom-modules-sha-overview"
module_enablement_state: ENABLED
- policy_id: CMEK key is use for Vertex AI DataLabelingJob
compliance_standards:
- standard: NIST SP 800-53
control: SC-12
- standard: NIST SP 800-53
control: SC-13
constraint:
security_health_analytics_custom_module:
display_name: "vertexAIDataLabelingJobCMEKDisabled"
config:
customOutput: {}
predicate:
expression: "!has(resource.encryptionSpec)"
resource_selector:
resource_types:
- aiplatform.googleapis.com/DataLabelingJob
severity: CRITICAL
description: "When enforced, this detector finds if any Data Set is not encrypted using CMEK. CMEKs, managed via Cloud KMS, offer advanced control over key operations."
recommendation: "Restore SHA module- Reset the SHA module to its intended state. Consult documentation- Refer to the comprehensive guidance provided at
https://cloud.google.com/security-command-center/docs/custom-modules-sha-overview"
module_enablement_state: ENABLED
- policy_id: CMEK key is use for Vertex AI CustomJob
compliance_standards:
- standard: NIST SP 800-53
control: SC-12
- standard: NIST SP 800-53
control: SC-13
constraint:
security_health_analytics_custom_module:
display_name: "vertexAICustomJobCMEKDisabled"
config:
customOutput: {}
predicate:
expression: "!has(resource.encryptionSpec)"
resource_selector:
resource_types:
- aiplatform.googleapis.com/CustomJob
severity: CRITICAL
description: "When enforced, this detector finds if any Data Set is not encrypted using CMEK. CMEKs, managed via Cloud KMS, offer advanced control over key operations."
recommendation: "Restore SHA module- Reset the SHA module to its intended state. Consult documentation- Refer to the comprehensive guidance provided at
https://cloud.google.com/security-command-center/docs/custom-modules-sha-overview"
module_enablement_state: ENABLED
- policy_id: CMEK key is use for Vertex AI HyperparameterTuningJob
compliance_standards:
- standard: NIST SP 800-53
control: SC-12
- standard: NIST SP 800-53
control: SC-13
constraint:
security_health_analytics_custom_module:
display_name: "vertexAIDataLabelingJobHyperparameterTuningJobCMEKDisabled"
config:
customOutput: {}
predicate:
expression: "!has(resource.encryptionSpec)"
resource_selector:
resource_types:
- aiplatform.googleapis.com/HyperparameterTuningJob
severity: CRITICAL
description: "When enforced, this detector finds if any Data Set is not encrypted using CMEK. CMEKs, managed via Cloud KMS, offer advanced control over key operations."
recommendation: "Restore SHA module- Reset the SHA module to its intended state. Consult documentation- Refer to the comprehensive guidance provided at
https://cloud.google.com/security-command-center/docs/custom-modules-sha-overview"
module_enablement_state: ENABLED