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    Using Sensitive Data Protection to scan BigQuery data

    Knowing where your sensitive data exists is often the first step in ensuring that it is properly secured and managed. This knowledge can help reduce the risk of exposing sensitive details such as credit card numbers, medical information, Social Security numbers, driver's license numbers, addresses, full names, and company-specific secrets. Periodic scanning of your data can also help with compliance requirements and ensure best practices are followed as your data grows and changes with use. To help meet compliance requirements, use Sensitive Data Protection to inspect your BigQuery tables and to help protect your sensitive data.

    There are two ways to scan your BigQuery data:

    • Sensitive data profiling.Sensitive Data Protection can generate profiles about BigQuery data across an organization, folder, or project. Data profiles contain metrics and metadata about your tables and help you determine where sensitive and high-risk data reside. Sensitive Data Protection reports these metrics at the project, table, and column levels. For more information, see Data profiles for BigQuery data .

    • On-demand inspection.Sensitive Data Protection can perform a deep inspection on a single table or a subset of columns and report its findings down to the cell level. This kind of inspection can help you identify individual instances of specific data types , such as the precise location of a credit card number inside a table cell. You can do an on-demand inspection through the Sensitive Data Protection page in the Google Cloud console, the BigQuerypage in the Google Cloud console, or programmatically through the DLP API.

    This page describes how to do an on-demand inspection through the BigQuerypage in the Google Cloud console.

    Sensitive Data Protection is a fully managed service that lets Google Cloud customers identify and protect sensitive data at scale. Sensitive Data Protection uses more than 150 predefined detectors to identify patterns, formats, and checksums. Sensitive Data Protection also provides a set of tools to de-identify your data including masking, tokenization, pseudonymization, date shifting, and more, all without replicating customer data.

    To learn more about Sensitive Data Protection, see the Sensitive Data Protection documentation.

    Before you begin

    1. Get familiar with Sensitive Data Protection pricing and how to keep Sensitive Data Protection costs under control .

    2. Enable the DLP API .

      Enable the API

    3. Ensure that the user creating your Sensitive Data Protection jobs is granted an appropriate predefined Sensitive Data Protection IAM role or sufficient permissions to run Sensitive Data Protection jobs.

    Scanning BigQuery data using the Google Cloud console

    To scan BigQuery data, you create a Sensitive Data Protection job that analyzes a table. You can scan a BigQuery table quickly by using the Scan with Sensitive Data Protectionoption in the BigQuery Google Cloud console.

    To scan a BigQuery table using Sensitive Data Protection:

    1. In the Google Cloud console, go to the BigQuery page.

      Go to BigQuery

    2. In the Explorerpanel, expand your project and dataset, then select the table.

    3. Click Export > Scan with Sensitive Data Protection. The Sensitive Data Protection job creation page opens in a new tab.

    4. For Step 1: Choose input data, enter a job ID. The values in the Locationsection are automatically generated. Also, the Samplingsection is automatically configured to run a sample scan against your data, but you can adjust the settings as needed.

    5. Click Continue.

    6. Optional: For Step 2: Configure detection, you can configure what types of data to look for, called infoTypes .

      Do one of the following:

      • To select from the list of predefined infoTypes , click Manage infoTypes. Then, select the infoTypes you want to search for.
      • To use an existing inspection template , in the Template namefield, enter the template's full resource name.

      For more information on infoTypes , see InfoTypes and infoType detectors in the Sensitive Data Protection documentation.

    7. Click Continue.

    8. Optional: For Step 3: Add actions, turn on Save to BigQueryto publish your Sensitive Data Protection findings to a BigQuery table. If you don't store findings, the completed job contains only statistics about the number of findings and their infoTypes . Saving findings to BigQuery saves details about the precise location and confidence of each individual finding.

    9. Optional: If you turned on Save to BigQuery, in the Save to BigQuerysection, enter the following information:

      • Project ID: the project ID where your results are stored.
      • Dataset ID: the name of the dataset that stores your results.
      • 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_ date _1234567890 . If you specify an existing table, findings are appended to it.

      To include the actual content that was detected, turn on Include quote.

    10. Click Continue.

    11. Optional: For Step 4: Schedule, configure a time span or schedule by selecting either Specify time spanor Create a trigger to run the job on a periodic schedule.

    12. Click Continue.

    13. Optional: On the Reviewpage, examine the details of your job. If needed, adjust the previous settings.

    14. Click Create.

    15. After the Sensitive Data Protection job completes, you are redirected to the job details page, and you're notified by email. You can view the results of the scan on the job details page, or you can click the link to the Sensitive Data Protection job details page in the job completion email.

    16. If you chose to publish Sensitive Data Protection findings to BigQuery, on the Job detailspage, click View Findings in BigQueryto open the table in the Google Cloud console. You can then query the table and analyze your findings. For more information on querying your results in BigQuery, see Querying Sensitive Data Protection findings in BigQuery in the Sensitive Data Protection documentation.

    What's next

    If you want to redact or otherwise de-identify the sensitive data that the Sensitive Data Protection scan found, see the following:
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