Postura predefinida para IA segura estendida

Esta página descreve as políticas de prevenção e detetive incluídas no a versão v1.0 da postura predefinida para IA segura estendida. Esta postura inclui dois conjuntos de políticas:

  • Um conjunto de políticas que inclui as políticas da organização para cargas de trabalho da Vertex AI.

  • Um conjunto de políticas que inclui detectores personalizados da Análise de integridade da segurança aplicáveis a para cargas de trabalho da Vertex AI.

É possível usar essa postura predefinida para definir uma postura de segurança que ajude a proteger o Gemini e aos recursos da Vertex AI. Se você quiser implantar essa postura predefinida, personalizar algumas das políticas para que elas se apliquem ao seu ambiente.

Restrições da política da organização

A tabela a seguir descreve as políticas da organização incluídas em essa postura.

Política Descrição Padrão de conformidade
ainotebooks.accessMode

Essa restrição define os modos de acesso que são permitidos ao Vertex AI Workbench como notebooks e instâncias.

É preciso configurar esse valor ao adotar essa postura predefinida.

 Controle do NIST SP 800-53: AC-3(3) e AC-6(1)
ainotebooks.disableFileDownloads

Essa restrição impede a criação de instâncias do Vertex AI Workbench com o arquivo. opção de download ativada. Por padrão, a opção de download de arquivos pode ser ativada em em qualquer instância do Vertex AI Workbench.

O valor é de true a Desativar os downloads de arquivos em novas instâncias do Vertex AI Workbench.

 Controle do NIST SP 800-53: AC-3(1)
ainotebooks.disableRootAccess

Essa restrição impede notebooks e instâncias recém-criados do Vertex AI Workbench gerenciados pelos usuários de ativar o acesso à raiz. Por padrão, o Vertex AI Workbench gerenciado pelo usuário como notebooks e instâncias podem ter acesso raiz ativado.

O valor é true para desativar o acesso raiz no novo Vertex AI Workbench notebooks e instâncias gerenciados pelo usuário.

 Controle do NIST SP 800-53: AC-3 e AC-6(2)
ainotebooks.disableTerminal

Essa restrição impede a criação de instâncias do Vertex AI Workbench com o terminal ativado. Por padrão, o terminal pode ser ativado instâncias do Vertex AI Workbench.

O valor é de true a desativar o terminal em novas instâncias do Vertex AI Workbench.

 Controle do NIST SP 800-53: AC-3, AC-6 e CM-2
ainotebooks.environmentOptions

Essa restrição define as opções de imagem de VM e de contêiner que um usuário pode selecionar ao criar novos instâncias e notebooks do Vertex AI Workbench em que essa restrição é aplicada. aplicadas. Para ser permitido ou negado, as opções precisam ser listadas explicitamente.

Os valores são os seguintes:

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
Controle do NIST SP 800-53: AC-3, AC-6 e CM-2
ainotebooks.requireAutoUpgradeSchedule

Essa restrição requer que novos notebooks gerenciados pelo usuário do Vertex AI Workbench e instâncias têm uma programação de upgrade automático definida.

O valor é true para exigir upgrades programados automáticos nos novos Notebooks e instâncias do Vertex AI Workbench gerenciados pelos usuários.

 Controle do NIST SP 800-53: AU-9, CM-2 e CM-6
ainotebooks.restrictPublicIp

Essa restrição restringe acesso de IP público a notebooks recém-criados do Vertex AI Workbench e instâncias. Por padrão, os IPs públicos podem acessar os notebooks do Vertex AI Workbench e instâncias.

O valor é true para restringir o acesso de IP público em novos notebooks e instâncias do Vertex AI Workbench.

 Controle do NIST SP 800-53: AC-3, AC-4 e SC-7
ainotebooks.restrictVpcNetworks

Essa lista define Redes VPC que um usuário pode selecionar ao criar um novo Vertex AI Workbench instâncias em que essa restrição é aplicada.

É preciso configurar esse valor ao adotar essa postura predefinida.

 Controle do NIST SP 800-53: AC-3, AC-4 e CM-2

Detectores do Security Health Analytics

A tabela a seguir descreve os módulos personalizados da Análise de integridade da segurança que são incluídos na postura predefinida.

Nome do detector Recurso aplicável Descrição Padrões de compliance
vertexAIDataSetCMEKDisabled aiplatform.googleapis.com/Dataset

Este detector verifica se os conjuntos de dados não forem criptografados com uma chave de criptografia gerenciada pelo cliente (CMEK).

Para resolver essa descoberta, verifique se você criou a chave e definir permissões e fornecer a chave quando você criou o no conjunto de dados. Para instruções, consulte Configurar a CMEK para seu do Google Cloud.

 Controle do NIST SP 800-53: SC12 e SC13
vertexAIModelCMEKDisabled aiplatform.googleapis.com/Model

Este detector verifica se um modelo não está criptografado usando uma CMEK.

Para resolver essa descoberta, verifique se você criou a chave e definir permissões e fornecer a chave quando você criou o um modelo de machine learning. Para instruções, consulte Configurar a CMEK para seu do Google Cloud.

 Controle do NIST SP 800-53: SC12 e SC13
vertexAIEndpointCMEKDisabled aiplatform.googleapis.com/Endpoint

Este detector verifica se um endpoint não está criptografado usando uma CMEK.

Para resolver essa descoberta, verifique se você criou a chave e definir permissões e fornecer a chave quando você criou o endpoint do Google Cloud. Para instruções, consulte Configurar a CMEK para seu do Google Cloud.

 Controle do NIST SP 800-53: SC12 e SC13
vertexAITrainingPipelineCMEKDisabled aiplatform.googleapis.com/TrainingPipeline

Esse detector verifica se um pipeline de treinamento não está criptografado com uma CMEK.

Para resolver essa descoberta, verifique se você criou a chave e definir permissões e fornecer a chave quando você criou o pipeline de treinamento. Para instruções, consulte Configurar a CMEK para seu do Google Cloud.

 Controle do NIST SP 800-53: SC12 e SC13
vertexAIDataLabelingJobCMEKDisabled aiplatform.googleapis.com/DataLabelingJob

Este detector verifica se um rótulo de dados não foi criptografado usando uma CMEK.

Para resolver essa descoberta, verifique se você criou a chave e definir permissões e fornecer a chave quando você criou o rótulo de dados. Para instruções, consulte Configurar a CMEK para seu do Google Cloud.

 Controle do NIST SP 800-53: SC12 e SC13
vertexAICustomJobCMEKDisabled aiplatform.googleapis.com/CustomJob

Esse detector verifica se um job que executa uma carga de trabalho personalizada não está criptografado usando uma CMEK.

Para resolver essa descoberta, verifique se você criou a chave e definir permissões e fornecer a chave quando você criou o job personalizado. Para instruções, consulte Configurar a CMEK para seu do Google Cloud.

 Controle do NIST SP 800-53: SC12 e SC13
vertexAIDataLabelingJobHyperparameterTuningJobCMEKDisabled aiplatform.googleapis.com/HyperparameterTuningJob

Esse detector verifica se um job de ajuste de hiperparâmetros não foi criptografado usando uma CMEK.

Para resolver essa descoberta, verifique se você criou a chave e definir permissões e fornecer a chave quando você criou o um job de ajuste de hiperparâmetros. Para instruções, consulte Configurar a CMEK para seu do Google Cloud.

 Controle do NIST SP 800-53: SC12 e SC13

Definição de YAML

Confira a seguir a definição de YAML para a postura predefinida de IA segura.

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

A seguir