How-to Guides

• Inspecting text for sensitive data

  Detect and classify sensitive information contained within text strings and text files.

• Inspecting structured text for sensitive data

  Detect and classify sensitive information contained within structured text strings.

• Inspecting images for sensitive data

  Detect and classify sensitive information contained within images.

• Inspecting storage and databases for sensitive data

  Detect and classify sensitive information contained within content stored in Google Cloud Storage, Cloud Datastore, and BigQuery.

• Creating inspection templates

  Use templates to create and persist inspect job configuration information.

• Listing supported predefined detectors

  Programmatically retrieve a list of all predefined infoTypes.

• Creating custom infoType detectors

  Create your own information type detectors to use for inspection and redaction.

• Creating a regular custom dictionary detector

  Create your own regular dictionary custom detectors to use for inspection and redaction.

• Creating a stored custom dictionary detector

  Create large dictionary custom detectors to inspect storage repositories.

• Creating a custom regex detector

  Create your own regular expression detectors to use for inspection and redaction.

• Modifying infoType detectors to refine scan results

  Refine the scan results that Cloud DLP returns by modifying the detection mechanism of a given infoType detector.

• Customizing match likelihood

  Use hotword context rules to extend your custom infoType detectors.

• Redacting sensitive data from text content

  Redact sensitive information from text strings.

• Redacting sensitive data from images

  Redact sensitive information from images.

• De-identifying sensitive data in text content

  Detect and de-identify sensitive data in text content.

• Creating de-identification templates

  Use templates to create and persist de-identification job configuration information.

• Measuring re-identification and disclosure risk

  Compute the likelihood that de-identified data will be re-identified, the risk of sensitive attribute disclosure, and the risk of dataset membership disclosure.

• Visualizing re-identification risk using k-anonymity

  Measure the k-anonymity of a dataset using Cloud Data Loss Prevention (DLP) and visualize it in Google Data Studio to determine the re-identifiability of the data.

• Computing numerical and categorical statistics

  Compute numerical and categorical numerical statistics for individual columns in BigQuery tables.

• Creating job triggers

  Set off Cloud DLP scan jobs automatically by setting up job triggers.

• Sending Cloud DLP scan results to Cloud SCC

  Instruct Cloud DLP to send results directly to Cloud Security Command Console (Cloud SCC).

• Analyzing and reporting on Cloud DLP scan findings

  Generate reports and run rich SQL analytics based on Cloud DLP scan findings.