Cloud Data Loss Prevention (Cloud DLP) 現已併入機密資料保護。API 名稱維持不變：Cloud Data Loss Prevention API (DLP API)。如要瞭解構成 Sensitive Data Protection 的服務，請參閱「Sensitive Data Protection 總覽」。
Sensitive Data Protection 可協助您瞭解、管理及保護機密資料。
透過機密資料保護服務，您可以輕鬆分類與遮蓋文字內容和圖片內含的機密資料，包括儲存在Google Cloud 儲存空間存放區的內容。
文字分類
如果是下列的文字輸入：
Please update my records with the following information:
Email address: foo@example.com
National Provider Identifier: 1245319599
Driver's license: AC333991
Please update my records with the following information:
Email address: foo@example.com
National Provider Identifier: 1245319599
Driver's license: AC333991
使用 "***" 預留位置的輸出範例：
Please update my records with the following information:
Email address: ***
National Provider Identifier: ***
Driver's license: ***
圖片分類
Sensitive Data Protection 使用光學字元辨識 (OCR) 技術，會在分類之前先辨識文字。和文字分類的做法類似，Cloud DLP 會傳回發現項目，但也會在找到的文字處加入定界框。
[[["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：2025-09-04 (世界標準時間)。"],[],[],null,["# Classification, redaction, and de-identification\n\nThe Sensitive Data Protection helps you understand, manage, and protect sensitive data.\nWith the Sensitive Data Protection, you can easily classify and redact sensitive\ndata contained in text-based content and images, including content stored in\nGoogle Cloud storage repositories.\n\nText classification\n-------------------\n\nGiven the following text input: \n\n```\nPlease update my records with the following information:\nEmail address: foo@example.com\n\nNational Provider Identifier: 1245319599\n\nDriver's license: AC333991\n```\n\nThe output is a list of findings, organized into the following categories:\n\n- [`InfoType`](/sensitive-data-protection/docs/infotypes-reference \"InfoTypes Reference\")\n- [`Likelihood`](/sensitive-data-protection/docs/likelihood \"Likelihood Concept Page\")\n- `Offset` (Where in the string the potential `InfoType` was found)\n\nExample output is shown in the table below.\n\nAutomatic text redaction\n------------------------\n\nAutomatic redaction produces an output with sensitive data matches removed\ninstead of giving you a list of findings.\n\nExample automation redaction input: \n\n```\nPlease update my records with the following information:\nEmail address: foo@example.com\n\nNational Provider Identifier: 1245319599\n\nDriver's license: AC333991\n```\n\nExample output using a placeholder of \"\\*\\*\\*\": \n\n```\nPlease update my records with the following information:\nEmail address: ***\n\nNational Provider Identifier: ***\n\nDriver's license: ***\n```\n\nImage classification\n--------------------\n\nSensitive Data Protection uses Optical Character Recognition (OCR)\ntechnology to recognize text prior to classification. Similar to text\nclassification, it returns findings, but it also adds a bounding box\nwhere the text was found.\n\nStorage classification\n----------------------\n\nStorage classification scans data stored in Cloud Storage, Firestore in Datastore mode (Datastore),\nand BigQuery. Instead of streaming data into Sensitive Data Protection, you\nspecify in your request the storage location for the Cloud Storage\nbucket, Datastore kind, or BigQuery table you\nwant Sensitive Data Protection to scan.\n\nWhen scanning files in Cloud Storage locations, Sensitive Data Protection\nsupports scanning of binary, text, image, Microsoft Word, Microsoft Excel,\nMicrosoft Powerpoint, PDF, and Apache Avro\nfiles. A list of file extensions for the file types within Cloud Storage\nthat Sensitive Data Protection can scan is available on the API reference page for\n[`FileType`](/sensitive-data-protection/docs/reference/rest/v2/FileType).\nFiles of types that are unrecognized are scanned as binary files.\n\nThe results of the scan can be either saved to a new BigQuery\ntable or published to a Pub/Sub topic. From there, you can use\nbuilt-in BigQuery tools to run rich SQL analytics or tools\nsuch as Looker Studio to generate reports.\n\nFor more information about scanning storage repositories for sensitive data\nusing Sensitive Data Protection, see [Inspecting storage and databases for\nsensitive data](/sensitive-data-protection/docs/inspecting-storage).\n\nFor more information about visualizing scan results using other Google Cloud\ntools, see [Analyzing and reporting on Sensitive Data Protection\nfindings](/sensitive-data-protection/docs/analyzing-and-reporting).\n\nWhat's next\n-----------\n\n- Learn more about [image inspection and redaction](/sensitive-data-protection/docs/concepts-image-redaction).\n- Learn about [transformation methods](/sensitive-data-protection/docs/transformations-reference) that you can use with Sensitive Data Protection.\n- Work through the [Redacting Sensitive Data with Sensitive Data Protection](https://www.cloudskillsboost.google/focuses/46234?parent=catalog) codelab.\n- Learn more about [creating a de-identified copy of data in\n storage](/sensitive-data-protection/docs/concepts-deidentify-storage)."]]
