Enable inspection or risk analysis actions

This document describes the actions that Sensitive Data Protection can perform after running an inspection job or risk analysis.

An action is a task that Sensitive Data Protection performs after completing an inspection job or risk analysis. For example, you can save findings to a BigQuery table, publish a notification to a Pub/Sub topic, or send an email when an operation either finishes successfully or stops on error.

Sensitive data discovery operations have a different set of actions. For more information about discovery actions, see Enable discovery actions.

Available actions

When you run a Sensitive Data Protection job, a summary of its findings are saved by default within Sensitive Data Protection. You can see this summary using Sensitive Data Protection in the Google Cloud console. You can also retrieve summary information in the DLP API by using the projects.dlpJobs.get method.

The following sections describe the actions that are available to inspection and risk analysis jobs.

Save findings to BigQuery

Save the Sensitive Data Protection job results to a BigQuery table. Before viewing or analyzing the results, first verify that the job has completed.

Each time a scan runs, Sensitive Data Protection saves scan findings to the BigQuery table that you specify. The exported findings contain details about each finding's location and match likelihood.

If you want each finding to include the string that matched the infoType detector, enable the Include quote option. Quotes are potentially sensitive, so Sensitive Data Protection doesn't include them in findings by default.

If you don't specify a table ID, BigQuery assigns a default name to a new table the first time the scan runs. The name is similar to dlpgoogleapisDATE_1234567890, where DATE represents the date the scan is run. If you specify an existing table, Sensitive Data Protection appends scan findings to it.

When data is written to a BigQuery table, the billing and quota usage are applied to the project that contains the destination table.

Save findings to Cloud Storage

Save the Sensitive Data Protection job results to an existing Cloud Storage bucket or folder. Before viewing or analyzing the results, first verify that the job has completed.

If you're inspecting a Cloud Storage bucket, the bucket that you designate for exported findings must not be the bucket that you're inspecting.

Each time a scan runs, Sensitive Data Protection saves scan findings to the Cloud Storage location that you specify. The exported findings contain details about each finding's location and match likelihood.

If you want each finding to include the string that matched the infoType detector, enable the Include quote option. Quotes are potentially sensitive, so Sensitive Data Protection doesn't include them in findings by default.

The findings are exported in Protobuf text format as a SaveToGcsFindingsOutput object. For information about how to parse findings in this format, see Parse findings stored as Protobuf text.

Publish to Pub/Sub

Publish a notification that contains the name of the Sensitive Data Protection job as an attribute to a Pub/Sub channel. You can specify one or more topics to send the notification message to. Make sure that the Sensitive Data Protection service account running the scan job has publishing access on the topic.

If there are configuration or permission issues with the Pub/Sub topic, Sensitive Data Protection retries sending the Pub/Sub notification for up to two weeks. After two weeks, the notification is discarded.

Publish to Security Command Center

Publish a summary of the job results to Security Command Center. For more information, see Send Sensitive Data Protection scan results to Security Command Center.

To use this action, your project must belong to an organization, and Security Command Center must be activated at the organization level. Otherwise, Sensitive Data Protection findings won't appear in Security Command Center. For more information, see Check the activation level of Security Command Center.

Publish to Data Catalog

Send job results to Data Catalog. This feature is deprecated.

Notify by email

Send an email when the job completes. The email goes to IAM project owners and technical Essential Contacts.

Publish to Cloud Monitoring

Send inspection results to Cloud Monitoring in Google Cloud Observability.

Make a de-identified copy

De-identify any findings in the inspected data, and write the de-identified content to a new file. You can then use the de-identified copy in your business processes, in place of data that contains sensitive information. For more information, see Create a de-identified copy of Cloud Storage data using Sensitive Data Protection in the Google Cloud console.

Supported operations

The following table shows the Sensitive Data Protection operations and where each action is available.

Action BigQuery inspection Cloud Storage inspection Datastore inspection Hybrid inspection Risk analysis
Save findings to BigQuery ✓ ✓ ✓ ✓ ✓
Save findings to Cloud Storage ✓ ✓ ✓ ✓
Publish to Pub/Sub ✓ ✓ ✓ ✓ ✓
Publish to Security Command Center ✓ ✓ ✓
Publish to Data Catalog (Deprecated) ✓
Notify by email ✓ ✓ ✓ ✓ ✓
Publish to Cloud Monitoring ✓ ✓ ✓ ✓
De-identify findings ✓

Specify actions

You can specify one or more actions when you configure a job:

  • When you create a new inspection or risk analysis job using Sensitive Data Protection in the Google Cloud console, specify actions in the Add actions section of the job creation workflow.
  • When you configure a new job request to send to the DLP API, specify actions in the Action object.

For more information and sample code in several languages, see:

Example action scenario

You can use Sensitive Data Protection actions to automate processes based on Sensitive Data Protection scan results. Suppose you have a BigQuery table shared with an external partner. You want to ensure both that this table does not contain any sensitive identifiers like US Social Security numbers (the infoType US_SOCIAL_SECURITY_NUMBER), and that, if you find any, access is revoked from the partner. Here is a rough outline of a workflow that would use actions:

  1. Create a Sensitive Data Protection job trigger to run an inspection scan of the BigQuery table every 24 hours.
  2. Set the action of these jobs to publish a Pub/Sub notification to the topic "projects/foo/scan_notifications."
  3. Create a Cloud Function that listens for incoming messages on "projects/foo/scan_notifications." This Cloud Function will receive the name of the Sensitive Data Protection job every 24 hours, call Sensitive Data Protection to get summary results from this job, and, if it finds any Social Security numbers, it can change settings in BigQuery or Identity and Access Management (IAM) to restrict access to the table.

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