瞭解自己的機密資料位於何處，通常是確認能夠妥善保護和管理這些資料的第一步。知道這點有助於降低信用卡號碼、醫療資訊、身分證字號、駕照號碼、地址、全名以及公司特定機密等敏感詳細資訊的暴露風險。此外，定期掃描資料也有助符合法規遵循要求，並確保您可以在資料隨著使用而增加或改變時，採行最佳做法。請使用 Sensitive Data Protection 檢查 BigQuery 資料表，並保護機密資料，以符合法規遵循要求。
您可以透過兩種方式掃描 BigQuery 資料：
機密資料分析。Sensitive Data Protection 可針對機構、資料夾或專案中的 BigQuery 資料產生剖析檔。資料剖析檔包含資料表的指標和中繼資料，可協助您判斷機密和高風險資料的存放位置。機密資料保護會在專案、資料表和資料欄層級回報這些指標。詳情請參閱「BigQuery 資料的資料分析器」。
隨選檢查。機密資料保護功能可對單一資料表或一小部分欄進行深入檢查，並將檢查結果回報至儲存格層級。這類檢查可協助您找出特定資料類型的個別例項，例如表格儲存格內信用卡號碼的確切位置。您可以透過Google Cloud 控制台的「Sensitive Data Protection」頁面、「BigQuery」BigQuery頁面，或是透過 DLP API 的程式碼進行檢查。 Google Cloud
本頁面說明如何透過 Google Cloud 控制台的「BigQuery」BigQuery頁面，執行按需檢查作業。
Sensitive Data Protection 是一項全代管服務，可讓 Google Cloud 客戶大規模識別及保護機密資料。機密資料保護會運用超過 150 項預先定義的偵測工具來識別模式、格式和總和檢查碼。此外，Sensitive Data Protection 還會提供一組資料去識別化的工具，包括遮蓋、代碼化、匿名化、日期轉移等做法，這些工具都不需要複製客戶資料。
[[["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)。"],[[["\u003cp\u003eSensitive Data Protection can scan BigQuery data to identify sensitive information, including credit card numbers, medical details, and other personal data, helping ensure its proper security and management.\u003c/p\u003e\n"],["\u003cp\u003eThere are two primary methods for scanning BigQuery data: sensitive data profiling, which provides an overview of data sensitivity across an organization, and on-demand inspection, which offers deep analysis of specific tables or columns down to the cell level.\u003c/p\u003e\n"],["\u003cp\u003eOn-demand inspections can be initiated from the BigQuery page in the Google Cloud console, allowing users to quickly analyze a table for sensitive data and configure what data types to look for.\u003c/p\u003e\n"],["\u003cp\u003eAfter completing a scan, Sensitive Data Protection can save the findings to a BigQuery table, including precise locations and confidence levels of sensitive data, providing detailed insights for further analysis or action.\u003c/p\u003e\n"],["\u003cp\u003eThe DLP API is required to be enabled, and users running Sensitive Data Protection jobs need appropriate IAM roles or permissions.\u003c/p\u003e\n"]]],[],null,["# Using Sensitive Data Protection to scan BigQuery data\n=====================================================\n\nKnowing where your sensitive data exists is often the first step in ensuring\nthat it is properly secured and managed. This knowledge can help reduce the risk\nof exposing sensitive details such as credit card numbers, medical information,\nSocial Security numbers, driver's license numbers, addresses, full names, and\ncompany-specific secrets. Periodic scanning of your data can also help with\ncompliance requirements and ensure best practices are followed as your data\ngrows and changes with use. To help meet compliance requirements, use\nSensitive Data Protection to inspect your BigQuery tables and\nto help protect your sensitive data.\n\nThere are two ways to scan your BigQuery data:\n\n- **Sensitive data profiling.** Sensitive Data Protection can generate profiles about\n BigQuery data across an organization, folder, or project. *Data\n profiles* contain metrics and metadata about your tables and help you\n determine where [sensitive and high-risk\n data](/sensitive-data-protection/docs/sensitivity-risk-calculation) reside. Sensitive Data Protection\n reports these metrics at the project, table, and column levels. For more\n information, see [Data profiles for\n BigQuery data](/sensitive-data-protection/docs/data-profiles).\n\n- **On-demand inspection.** Sensitive Data Protection can perform a deep inspection on\n a single table or a subset of columns and report its findings down to the cell\n level. This kind of inspection can help you identify individual instances of\n specific data [types](/sensitive-data-protection/docs/infotypes-reference), such as the precise\n location of a credit card number inside a table cell. You can do an on-demand\n inspection through the Sensitive Data Protection page in the\n Google Cloud console, the **BigQuery** page in the Google Cloud console,\n or programmatically through the DLP API.\n\nThis page describes how to do an on-demand inspection through the\n**BigQuery** page in the Google Cloud console.\n\nSensitive Data Protection is a fully managed service that lets Google Cloud customers\nidentify and protect sensitive data at scale. Sensitive Data Protection uses more\nthan 150 predefined detectors to identify patterns, formats, and checksums.\nSensitive Data Protection also provides a set of tools to de-identify your data\nincluding masking, tokenization, pseudonymization, date shifting, and more, all\nwithout replicating customer data.\n\nTo learn more about Sensitive Data Protection, see the [Sensitive Data Protection](/sensitive-data-protection/docs)\ndocumentation.\n\nBefore you begin\n----------------\n\n1. Get familiar with [Sensitive Data Protection pricing](/sensitive-data-protection/pricing) and [how to keep Sensitive Data Protection costs under control](/sensitive-data-protection/docs/best-practices-costs).\n2. [Enable the DLP API](/apis/docs/enable-disable-apis).\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=dlp.googleapis.com)\n3. Ensure that the user creating your Sensitive Data Protection jobs is granted an\n appropriate predefined Sensitive Data Protection [IAM role](/sensitive-data-protection/docs/iam-roles) or\n sufficient [permissions](/sensitive-data-protection/docs/iam-permissions) to run Sensitive Data Protection\n jobs.\n\n| **Note:** When you enable the DLP API, a service account is created with a name similar to `service-`\u003cvar translate=\"no\"\u003eproject_number\u003c/var\u003e`@dlp-api.iam.gserviceaccount.com`. This service account is granted the DLP API Service Agent role, which lets the service account authenticate with the BigQuery API. For more information, see [Service account](/sensitive-data-protection/docs/iam-permissions#service_account) on the Sensitive Data Protection IAM permissions page.\n\nScanning BigQuery data using the Google Cloud console\n-----------------------------------------------------\n\nTo scan BigQuery data, you create a Sensitive Data Protection job\nthat analyzes a table. You can scan a BigQuery table quickly by using\nthe **Scan with Sensitive Data Protection** option in the BigQuery Google Cloud console.\n\nTo scan a BigQuery table using Sensitive Data Protection:\n\n1. In the Google Cloud console, go to the BigQuery page.\n\n [Go to BigQuery](https://console.cloud.google.com/bigquery)\n2. In the **Explorer** panel, expand your project and dataset, then select\n the table.\n\n3. Click **Export \\\u003e Scan with Sensitive Data Protection**. The Sensitive Data Protection job\n creation page opens in a new tab.\n\n4. For **Step 1: Choose input data** , enter a job ID. The values in the\n **Location** section are automatically generated. Also, the **Sampling**\n section is automatically configured to run a sample scan against your data, but\n you can adjust the settings as needed.\n\n5. Click **Continue**.\n\n6. Optional: For **Step 2: Configure detection** , you can configure what types\n of data to look for, called `infoTypes`.\n\n Do one of the following:\n - To select from the list of predefined `infoTypes`, click **Manage\n infoTypes**. Then, select the infoTypes you want to search for.\n - To use an existing [inspection template](/sensitive-data-protection/docs/creating-templates-inspect), in the **Template name** field, enter the template's full resource name.\n\n For more information on `infoTypes`, see\n [InfoTypes and infoType detectors](/sensitive-data-protection/docs/concepts-infotypes) in the\n Sensitive Data Protection documentation.\n7. Click **Continue**.\n\n8. Optional: For **Step 3: Add actions** , turn on **Save to BigQuery**\n to publish your Sensitive Data Protection findings to a BigQuery\n table. If you don't store findings, the completed job contains only\n statistics about the number of findings and their `infoTypes`. Saving\n findings to BigQuery saves details about the precise location and\n confidence of each individual finding.\n\n9. Optional: If you turned on **Save to BigQuery** , in the **Save to\n BigQuery** section, enter the following information:\n\n - **Project ID**: the project ID where your results are stored.\n - **Dataset ID**: the name of the dataset that stores your results.\n - Optional: **Table ID** : the name of the table that stores your results. If no table ID is specified, a default name is assigned to a new table similar to the following: `dlp_googleapis_`\u003cvar translate=\"no\"\u003edate\u003c/var\u003e`_1234567890`. If you specify an existing table, findings are appended to it.\n\n To include the actual content that was detected, turn on **Include quote**.\n10. Click **Continue**.\n\n11. Optional: For **Step 4: Schedule** , configure a time span or schedule by\n selecting either **Specify time span** or **Create a trigger to run the job\n on a periodic schedule**.\n\n12. Click **Continue**.\n\n13. Optional: On the **Review** page, examine the details of your job. If needed,\n adjust the previous settings.\n\n14. Click **Create**.\n\n15. After the Sensitive Data Protection job completes, you are redirected to the job\n details page, and you're notified by email. You can view the results of the\n scan on the job details page, or you can click the link to\n the Sensitive Data Protection job details page in the job completion email.\n\n16. If you chose to publish Sensitive Data Protection findings to\n BigQuery, on the **Job details** page, click **View Findings in\n BigQuery** to open the table in the Google Cloud console. You can then query the\n table and analyze your findings. For more information on querying your results\n in BigQuery, see\n [Querying Sensitive Data Protection findings in BigQuery](/sensitive-data-protection/docs/querying-findings)\n in the Sensitive Data Protection documentation.\n\nWhat's next\n-----------\n\n- Learn more about [inspecting BigQuery and other storage\n repositories for sensitive data using Sensitive Data Protection](/sensitive-data-protection/docs/inspecting-storage).\n\n- Learn more about [profiling data in an organization, folder, or\n project](/sensitive-data-protection/docs/data-profiles).\n\n- Read the Identity \\& Security blog post [Take charge of your data: using\n Sensitive Data Protection to de-identify and obfuscate sensitive\n information](https://cloud.google.com/blog/products/identity-security/taking-charge-of-your-data-using-cloud-dlp-to-de-identify-and-obfuscate-sensitive-information).\n\nIf you want to redact or otherwise de-identify the sensitive data that the\nSensitive Data Protection scan found, see the following:\n\n- [Inspect text to de-identify sensitive information](/sensitive-data-protection/docs/inspect-sensitive-text-de-identify)\n- [De-identifying sensitive data](/sensitive-data-protection/docs/deidentify-sensitive-data) in the Sensitive Data Protection documentation\n- [AEAD encryption concepts in GoogleSQL](/bigquery/docs/aead-encryption-concepts) for information on encrypting individual values within a table\n- [Protecting data with Cloud KMS keys](/bigquery/docs/customer-managed-encryption) for information on creating and managing your own encryption keys in [Cloud KMS](/kms/docs) to encrypt BigQuery tables"]]
