How-to Guides

• Inspecting Text for Sensitive Data

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

• 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 the DLP API 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 the Cloud Data Loss Prevention (DLP) API 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 DLP API scan jobs automatically by setting up job triggers.

• Analyzing and Reporting on DLP API Scan Findings

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