Sensitive Data Protection 是一項 Google Cloud 服務,可協助您探索、分類及去識別化機密資料。Sensitive Data Protection 可辨識機密元素、內容和文件,協助您降低 AI 工作負載資料外洩的風險。您可以在 Model Armor 中直接使用機密資料防護功能,轉換、權杖化及遮蓋敏感元素,同時保留非敏感內容。Model Armor 可接受現有的檢查範本,這些範本是類似藍圖的設定,可簡化掃描和識別業務及法規遵循需求相關機密資料的程序。這樣一來,您就能確保使用 Sensitive Data Protection 的其他工作負載之間維持一致性及互通性。
Model Armor 提供兩種模式,可設定 Sensitive Data Protection:
基本 Sensitive Data Protection 設定:這個模式可直接指定要掃描的私密/機密資料類型,簡化 Sensitive Data Protection 的設定程序。這項功能支援六個類別,分別是 CREDIT_CARD_NUMBER、US_SOCIAL_SECURITY_NUMBER、FINANCIAL_ACCOUNT_NUMBER、US_INDIVIDUAL_TAXPAYER_IDENTIFICATION_NUMBER、GCP_CREDENTIALS、GCP_API_KEY。基本設定僅允許檢查作業,不支援使用 Sensitive Data Protection 範本。詳情請參閱「基本 Sensitive Data Protection 設定」。
進階 Sensitive Data Protection 設定:這個模式可啟用 Sensitive Data Protection 範本,提供更靈活的自訂功能。機密資料保護範本是預先定義的設定,可讓您指定更精細的偵測規則和去識別化技術。進階設定支援檢查和去識別化作業。
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間:2025-09-05 (世界標準時間)。"],[],[],null,["This page provides information about the key concepts for\nModel Armor.\n\nModel Armor templates\n\nModel Armor templates let you configure how Model Armor\nscreens prompts and responses. They function as sets of customized filters and\nthresholds for different safety and security confidence levels, allowing control\nover what content is flagged.\n\nThe thresholds represent confidence levels. That is, how confident Model Armor\nis about the prompt or response including offending content. For example, you\ncan create a template that filters prompts for hateful content with a `HIGH`\nthreshold, meaning Model Armor reports high confidence that the prompt\ncontains hateful content. A `LOW_AND_ABOVE` threshold indicates any level of\nconfidence (`LOW`, `MEDIUM`, and `HIGH`) in making that claim.\n\nModel Armor filters\n\nModel Armor offers a variety of filters to help you provide safe and\nsecure AI models. Here's a breakdown of the filter categories.\n\nResponsible AI safety filter\n\nPrompts and responses can be screened at the aforementioned confidence levels\nfor the following categories:\n\n| Category | Definition |\n|-------------------|----------------------------------------------------------------------------------------|\n| Hate Speech | Negative or harmful comments targeting identity and/or protected attributes. |\n| Harassment | Threatening, intimidating, bullying, or abusive comments targeting another individual. |\n| Sexually Explicit | Contains references to sexual acts or other lewd content. |\n| Dangerous Content | Promotes or enables access to harmful goods, services, and activities. |\n\nThe child sexual abuse material (CSAM) filter is applied by default and\ncannot be turned off.\n\nPrompt injection and jailbreak detection\n\nPrompt injection is a security vulnerability where attackers craft special\ncommands within the text input (the prompt) to trick an AI model. This can\nmake the AI ignore its usual instructions, reveal sensitive information, or\nperform actions it wasn't designed to do. Jailbreaking in the context of LLMs\nrefers to the act of bypassing the safety protocols and ethical guidelines that\nare built into the model. This allows the LLM to generate responses that it was\noriginally designed to avoid, such as harmful, unethical, and dangerous content.\n\nWhen prompt injection and jailbreak detection is enabled, Model Armor\nscans prompts and responses for malicious content. If it is detected,\nModel Armor blocks the prompt or response.\n\nSensitive Data Protection\n\nSensitive data, like a person's name or address, may inadvertently or\nintentionally be sent to a model or provided in a model's response.\n\nSensitive Data Protection is a Google Cloud service to help you discover,\nclassify, and de-identify sensitive data. Sensitive Data Protection\ncan identify sensitive elements, context, and documents to help you reduce the risk of data leakage going into and\nout of AI workloads. You can use Sensitive Data Protection\ndirectly within Model Armor to transform, tokenize, and redact sensitive elements while retaining non-sensitive context.\nModel Armor can accept existing inspection templates,\nwhich are configurations that act like blueprints to streamline the process of\nscanning and identifying sensitive data specific to your business and compliance\nneeds. This way, you can have consistency and interoperability between other\nworkloads that use Sensitive Data Protection.\n\nModel Armor offers two modes for Sensitive Data Protection\nconfiguration:\n\n- Basic Sensitive Data Protection configuration: This mode provides a simpler\n way to configure Sensitive Data Protection by directly specifying the types\n of sensitive data to scan for. It supports six categories, which are,\n `CREDIT_CARD_NUMBER`, `US_SOCIAL_SECURITY_NUMBER`, `FINANCIAL_ACCOUNT_NUMBER`,\n `US_INDIVIDUAL_TAXPAYER_IDENTIFICATION_NUMBER`, `GCP_CREDENTIALS`, `GCP_API_KEY`.\n Basic configuration only allows for inspection operations and does not support\n the use of Sensitive Data Protection templates. For more information, see\n [Basic Sensitive Data Protection configuration](/security-command-center/docs/sanitize-prompts-responses#basic_sdp_configuration).\n\n- Advanced Sensitive Data Protection configuration: This mode offers more\n flexibility and customization by enabling the use of Sensitive Data Protection\n templates. Sensitive Data Protection templates are predefined configurations\n that allow you to specify more granular detection rules and de-identification\n techniques. Advanced configuration supports both inspection and de-identification\n operations.\n\nWhile confidence levels can be set for Sensitive Data Protection, they operate\nin a slightly different way than confidence levels for other filters. For more\ninformation about confidence levels for Sensitive Data Protection, see\n[Sensitive Data Protection match likelihood](/sensitive-data-protection/docs/likelihood).\nFor more information about Sensitive Data Protection in general, see\n[Sensitive Data Protection overview](/sensitive-data-protection/docs/sensitive-data-protection-overview).\n\nMalicious URL detection\n\nMalicious URLs are often disguised to look legitimate, making them a potent tool\nfor phishing attacks, malware distribution, and other online threats. For\nexample, if a PDF contains an embedded malicious URL, it can be used to\ncompromise any downstream systems processing LLM outputs.\n\nWhen malicious URL detection is enabled, Model Armor scans URLs\nto identify if they're malicious. This lets you to take action and prevent\nmalicious URLs from being returned.\n\nModel Armor confidence levels\n\nConfidence levels can be set for responsible AI safety categories (that is, Sexually Explicit,\nDangerous, Harassment, and Hate Speech), Prompt Injection and Jailbreak, and Sensitive\nData Protection (including topicality).\n| **Note:** While confidence levels can be set for Sensitive Data Protection, they operate in a slightly different way than confidence levels for other filters. For more information about confidence levels for Sensitive Data Protection, see [Sensitive Data Protection match likelihood](/sensitive-data-protection/docs/likelihood).\n\nFor confidence levels that allow granular thresholds, Model Armor\ninterprets them as follows:\n\n- High: Identify if the message has content with a high likelihood.\n- Medium and above: Identify if the message has content with a medium or high likelihood.\n- Low and above: Identify if the message has content with a low, medium, or high likelihood.\n\n| **Note:** Confidence levels are applicable only to [prompt injection and jailbreak detection](#ma-prompt-injection) and [responsible AI safety filters](#ma-responsible-ai-safety-categories).\n\nDefine the enforcement type\n\nEnforcement defines what happens after a violation is detected. To configure how\nModel Armor handles detections, you set the enforcement type.\nModel Armor offers the following enforcement types:\n\n- **Inspect only**: It inspects requests that violate the configured settings, but it doesn't block them.\n- **Inspect and block**: It blocks requests that violate the configured settings.\n\nTo effectively use `Inspect only` and gain valuable insights, enable Cloud Logging.\nWithout Cloud Logging enabled, `Inspect only` won't yield any useful information.\n\nAccess your logs through Cloud Logging. Filter by the service name\n`modelarmor.googleapis.com`. Look for entries related to the operations that you\nenabled in your template. For more information, see\n[View logs by using the Logs Explorer](/logging/docs/view/logs-explorer-interface).\n\nPDF screening\n\nText in PDFs can include malicious and sensitive content. Model Armor\ncan screen PDFs for safety, prompt injection and jailbreak attempts, sensitive data,\nand malicious URLs.\n\nModel Armor floor settings\n\nWhile Model Armor templates provide flexibility for individual\napplications, organizations often need to establish a baseline level of\nprotection across all their AI applications. This is where Model Armor\nfloor settings are used. They act as rules that dictate minimum requirements\nfor all templates created at a specific point in the Google Cloud resource\nhierarchy (that is, at an organization, folder, or project level).\n\nFor more information, see [Model Armor floor settings](/security-command-center/docs/model_armor_floor_settings).\n\nWhat's next\n\n- Learn about [Model Armor overview](/security-command-center/docs/model-armor-overview).\n- Learn about [Model Armor templates](/security-command-center/docs/manage-model-armor-templates).\n- Learn about [Model Armor floor settings](/security-command-center/docs/model_armor_floor_settings).\n- [Sanitize prompts and responses](/security-command-center/docs/sanitize-prompts-responses).\n- Learn about [Model Armor audit logging](/security-command-center/docs/audit-logging-model-armor).\n- [Troubleshoot Model Armor issues](/security-command-center/docs/troubleshooting#ma)."]]