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Model Armor terintegrasi dengan berbagai layanan Google Cloud :
Google Kubernetes Engine (GKE) dan Ekstensi Layanan
Vertex AI
GKE dan Ekstensi Layanan
Model Armor dapat diintegrasikan dengan GKE melalui Ekstensi Layanan. Ekstensi Layanan memungkinkan Anda mengintegrasikan layanan internal (layananGoogle Cloud ) atau eksternal (dikelola pengguna) untuk memproses traffic. Anda dapat mengonfigurasi ekstensi layanan di load balancer aplikasi, termasuk gateway inferensi GKE, untuk menyaring traffic ke dan dari cluster GKE. Hal ini memverifikasi bahwa semua interaksi dengan model AI dilindungi oleh Model Armor. Untuk mengetahui informasi selengkapnya, lihat
Integrasi dengan GKE.
Vertex AI
Model Armor dapat diintegrasikan langsung ke Vertex AI menggunakan
setelan batas bawah atau
template.
Integrasi ini menyaring permintaan dan respons model Gemini, memblokir permintaan dan respons yang melanggar setelan batas bawah. Integrasi ini memberikan perlindungan perintah dan respons dalam Gemini API di Vertex AI untuk metode generateContent. Anda harus mengaktifkan Cloud Logging untuk mendapatkan visibilitas
ke dalam hasil pembersihan perintah dan respons. Untuk mengetahui informasi selengkapnya, lihat
Integrasi dengan Vertex AI.
Sebelum memulai
Mengaktifkan API
Anda harus mengaktifkan Model Armor API sebelum dapat menggunakan Model Armor.
At the bottom of the Google Cloud console, a
Cloud Shell
session starts and displays a command-line prompt. Cloud Shell is a shell environment
with the Google Cloud CLI
already installed and with values already set for
your current project. It can take a few seconds for the session to initialize.
Jalankan perintah berikut untuk menetapkan endpoint API untuk layanan Model Armor.
Untuk opsi integrasi REST API, Model Armor berfungsi sebagai detektor
hanya menggunakan template. Artinya, alat ini terutama mengidentifikasi dan melaporkan potensi pelanggaran kebijakan berdasarkan template yang telah ditentukan, bukan secara aktif mencegahnya.
Dengan opsi integrasi Vertex AI, Model Armor memberikan penegakan inline menggunakan setelan atau template batas bawah. Artinya, Model Armor secara aktif menerapkan kebijakan dengan melakukan intervensi langsung dalam proses tanpa memerlukan modifikasi pada kode aplikasi Anda.
Mirip dengan Vertex AI, opsi integrasi GKE juga
menawarkan penerapan inline hanya menggunakan template. Hal ini menunjukkan bahwa
Model Armor dapat menerapkan kebijakan langsung dalam gateway inferensi
tanpa memerlukan modifikasi pada kode aplikasi Anda.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-09 UTC."],[],[],null,["# Model Armor integration with Google Cloud services\n\n| **Preview**\n|\n|\n| This feature is subject to the \"Pre-GA Offerings Terms\" in the General Service Terms section\n| of the [Service Specific Terms](/terms/service-terms#1).\n|\n| Pre-GA features are available \"as is\" and might have limited support.\n|\n| For more information, see the\n| [launch stage descriptions](/products#product-launch-stages).\n\nModel Armor integrates with various Google Cloud services:\n\n- Google Kubernetes Engine (GKE) and Service Extensions\n- Vertex AI\n\nGKE and Service Extensions\n--------------------------\n\nModel Armor can be integrated with GKE through\nService Extensions. Service Extensions allow you to integrate\ninternal (Google Cloud services) or external (user-managed) services to process\ntraffic. You can configure a service extension on application load balancers,\nincluding GKE inference gateways, to screen traffic to and from a\nGKE cluster. This verifies that all interactions with the AI models\nare protected by Model Armor. For more information, see\n[Integration with GKE](/security-command-center/docs/model-armor-gke-integration).\n\nVertex AI\n---------\n\nModel Armor can be directly integrated into Vertex AI using either\n[floor settings](/security-command-center/docs/model-armor-vertex-integration#configure-floor-settings) or\n[templates](/security-command-center/docs/model-armor-vertex-integration#configure-templates).\nThis integration screens Gemini model requests and responses, blocking\nthose that violate floor settings. This integration provides prompt and response\nprotection within Gemini API in Vertex AI for the\n`generateContent` method. You need to enable Cloud Logging to get visibility\ninto the sanitization results of prompts and responses. For more information, see\n[Integration with Vertex AI](/security-command-center/docs/model-armor-vertex-integration).\n\nBefore you begin\n----------------\n\n### Enable APIs\n\nYou must enable Model Armor APIs before you can use Model Armor. \n\n### Console\n\n1.\n\n\n Enable the Model Armor API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=modelarmor.googleapis.com)\n\n \u003cbr /\u003e\n\n2. Select the project where you want to activate Model Armor.\n\n### gcloud\n\nBefore you begin, follow these steps using the Google Cloud CLI with the\nModel Armor API:\n\n1.\n\n\n In the Google Cloud console, activate Cloud Shell.\n\n [Activate Cloud Shell](https://console.cloud.google.com/?cloudshell=true)\n\n\n At the bottom of the Google Cloud console, a\n [Cloud Shell](/shell/docs/how-cloud-shell-works)\n session starts and displays a command-line prompt. Cloud Shell is a shell environment\n with the Google Cloud CLI\n already installed and with values already set for\n your current project. It can take a few seconds for the session to initialize.\n\n \u003cbr /\u003e\n\n2. Run the following command to set the API endpoint for the\n Model Armor service.\n\n ```bash\n gcloud config set api_endpoint_overrides/modelarmor \"https://modelarmor.\u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e.rep.googleapis.com/\"\n ```\n\n Replace \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e with the region where you want to use Model Armor.\n\nRun the following command to enable Model Armor.\n\n\u003cbr /\u003e\n\n```bash\n gcloud services enable modelarmor.googleapis.com --project=PROJECT_ID\n \n```\n\n\u003cbr /\u003e\n\nReplace \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e with the ID of the project.\n\nOptions when integrating Model Armor\n------------------------------------\n\nModel Armor offers the following integration options. Each option provides different\nfeatures and capabilities.\n\nFor the REST API integration option, Model Armor functions as a detector\nonly using templates. This means it primarily identifies and reports potential\npolicy violations based on predefined templates, rather than actively preventing\nthem.\n\nWith the Vertex AI integration option, Model Armor provides\ninline enforcement using floor settings or templates. This means\nModel Armor actively enforces policies by intervening directly\nin the process without requiring modifications to your application code.\n\nSimilar to Vertex AI, the GKE integration option also\noffers inline enforcement only using templates. This indicates that\nModel Armor can enforce policies directly within the inference gateway\nwithout requiring modifications to your application code."]]
