Postur yang telah ditentukan sebelumnya untuk AI yang aman, diperluas

Halaman ini menjelaskan kebijakan preventif dan detektif yang disertakan dalam versi v1.0 postur yang telah ditentukan sebelumnya untuk AI yang aman dan diperluas. Postur ini mencakup dua set kebijakan:

  • Kumpulan kebijakan yang mencakup kebijakan organisasi yang berlaku untuk beban kerja Vertex AI.

  • Kumpulan kebijakan yang mencakup detektor Security Health Analytics kustom yang berlaku untuk workload Vertex AI.

Anda dapat menggunakan postur yang telah ditentukan ini untuk mengonfigurasi postur keamanan yang membantu melindungi resource Gemini dan Vertex AI. Jika ingin men-deploy postur yang telah ditentukan ini, Anda harus menyesuaikan beberapa kebijakan agar berlaku untuk lingkungan Anda.

Batasan kebijakan organisasi

Tabel berikut menjelaskan kebijakan organisasi yang disertakan dalam postur ini.

Kebijakan Deskripsi Standar kepatuhan
ainotebooks.accessMode

Batasan ini menentukan mode akses yang diizinkan untuk notebook dan instance Vertex AI Workbench.

Anda harus mengonfigurasi nilai ini saat menggunakan postur yang telah ditentukan ini.

 Kontrol NIST SP 800-53: AC-3(3) dan AC-6(1)
ainotebooks.disableFileDownloads

Batasan ini mencegah pembuatan instance Vertex AI Workbench dengan opsi download file diaktifkan. Secara default, opsi download file dapat diaktifkan pada instance Vertex AI Workbench apa pun.

Nilainya adalah true untuk menonaktifkan download file di instance Vertex AI Workbench yang baru.

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

Batasan ini mencegah instance dan notebook yang dikelola pengguna Vertex AI Workbench yang baru dibuat tidak mengaktifkan akses root. Secara default, instance dan notebook yang dikelola pengguna Vertex AI Workbench dapat mengaktifkan akses root.

Nilainya adalah true untuk menonaktifkan akses root di instance dan notebook yang dikelola pengguna Vertex AI Workbench baru.

 Kontrol NIST SP 800-53: AC-3 dan AC-6(2)
ainotebooks.disableTerminal

Batasan ini mencegah pembuatan instance Vertex AI Workbench dengan terminal yang diaktifkan. Secara default, terminal dapat diaktifkan di instance Vertex AI Workbench.

Nilainya adalah true untuk menonaktifkan terminal pada instance Vertex AI Workbench baru.

 Kontrol NIST SP 800-53: AC-3, AC-6, dan CM-2
ainotebooks.environmentOptions

Batasan ini menentukan opsi image container dan VM yang dapat dipilih pengguna saat membuat notebook Vertex AI Workbench baru dan instance tempat batasan ini diterapkan. Opsi untuk diizinkan atau ditolak harus dicantumkan secara eksplisit.

Nilainya adalah sebagai berikut:


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
Kontrol NIST SP 800-53: AC-3, AC-6, dan CM-2
ainotebooks.requireAutoUpgradeSchedule

Batasan ini mengharuskan notebook dan instance yang dikelola pengguna Vertex AI Workbench yang baru dibuat memiliki jadwal upgrade otomatis.

Nilainya adalah true untuk mewajibkan upgrade terjadwal otomatis pada instance dan notebook baru yang dikelola pengguna Vertex AI Workbench.

 Kontrol NIST SP 800-53: AU-9, CM-2, dan CM-6
ainotebooks.restrictPublicIp

Batasan ini membatasi akses IP publik ke notebook dan instance Vertex AI Workbench yang baru dibuat. Secara default, IP publik dapat mengakses notebook dan instance Vertex AI Workbench.

Nilainya adalah true untuk membatasi akses IP publik pada notebook dan instance Vertex AI Workbench baru.

 Kontrol NIST SP 800-53: AC-3, AC-4, dan SC-7
ainotebooks.restrictVpcNetworks

Daftar ini menentukan jaringan VPC yang dapat dipilih pengguna saat membuat instance Vertex AI Workbench baru tempat batasan ini diterapkan.

Anda harus mengonfigurasi nilai ini saat mengadopsi postur yang telah ditentukan ini.

 Kontrol NIST SP 800-53: AC-3, AC-4, dan CM-2

Pendeteksi Security Health Analytics

Tabel berikut menjelaskan modul kustom untuk Security Health Analytics yang disertakan dalam postur yang telah ditentukan.

Nama pendeteksi Referensi yang berlaku Deskripsi Standar kepatuhan
vertexAIDataSetCMEKDisabled aiplatform.googleapis.com/Dataset

Detektor ini memeriksa apakah ada set data yang tidak dienkripsi menggunakan kunci enkripsi yang dikelola pelanggan (CMEK).

Untuk mengatasi temuan ini, pastikan Anda telah membuat kunci dan key ring, menyiapkan izin, serta memberikan kunci saat membuat set data. Untuk mengetahui petunjuknya, lihat Mengonfigurasi CMEK untuk resource.

 Kontrol NIST SP 800-53: SC12 dan SC13
vertexAIModelCMEKDisabled aiplatform.googleapis.com/Model

Detektor ini memeriksa apakah model tidak dienkripsi menggunakan CMEK.

Untuk mengatasi temuan ini, pastikan Anda telah membuat kunci dan key ring, menyiapkan izin, serta menyediakan kunci saat membuat model. Untuk mengetahui petunjuknya, lihat Mengonfigurasi CMEK untuk resource.

 Kontrol NIST SP 800-53: SC12 dan SC13
vertexAIEndpointCMEKDisabled aiplatform.googleapis.com/Endpoint

Pendeteksi ini memeriksa apakah endpoint tidak dienkripsi menggunakan CMEK.

Untuk mengatasi temuan ini, pastikan Anda telah membuat kunci dan key ring, menyiapkan izin, serta memberikan kunci saat membuat endpoint. Untuk mengetahui petunjuknya, lihat Mengonfigurasi CMEK untuk resource.

 Kontrol NIST SP 800-53: SC12 dan SC13
vertexAITrainingPipelineCMEKDisabled aiplatform.googleapis.com/TrainingPipeline

Detektor ini memeriksa apakah pipeline pelatihan tidak dienkripsi menggunakan CMEK.

Untuk mengatasi temuan ini, pastikan Anda telah membuat kunci dan key ring, menyiapkan izin, serta memberikan kunci saat membuat pipeline pelatihan. Untuk mengetahui petunjuknya, lihat Mengonfigurasi CMEK untuk resource.

 Kontrol NIST SP 800-53: SC12 dan SC13
vertexAIDataLabelingJobCMEKDisabled aiplatform.googleapis.com/DataLabelingJob

Pendeteksi ini memeriksa apakah label data tidak dienkripsi menggunakan CMEK.

Untuk mengatasi temuan ini, pastikan Anda telah membuat kunci dan key ring, menyiapkan izin, serta memberikan kunci saat membuat label data. Untuk mengetahui petunjuknya, lihat Mengonfigurasi CMEK untuk resource.

 Kontrol NIST SP 800-53: SC12 dan SC13
vertexAICustomJobCMEKDisabled aiplatform.googleapis.com/CustomJob

Pendeteksi ini memeriksa apakah tugas yang menjalankan beban kerja kustom tidak dienkripsi menggunakan CMEK.

Untuk mengatasi temuan ini, pastikan Anda telah membuat kunci dan key ring, menyiapkan izin, serta memberikan kunci saat membuat tugas kustom. Untuk mengetahui petunjuknya, lihat Mengonfigurasi CMEK untuk resource.

 Kontrol NIST SP 800-53: SC12 dan SC13
vertexAIDataLabelingJobHyperparameterTuningJobCMEKDisabled aiplatform.googleapis.com/HyperparameterTuningJob

Detektor ini memeriksa apakah tugas penyesuaian hyperparameter tidak dienkripsi menggunakan CMEK.

Untuk mengatasi temuan ini, verifikasi bahwa Anda telah membuat kunci dan key ring, menyiapkan izin, serta memberikan kunci saat Anda membuat tugas penyesuaian hyperparameter. Untuk mengetahui petunjuknya, lihat Mengonfigurasi CMEK untuk resource.

 Kontrol NIST SP 800-53: SC12 dan SC13

Definisi YAML

Berikut adalah definisi YAML untuk postur standar untuk AI aman.

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

Langkah selanjutnya