Google Cloud’s Sensitive Data Protection service is a highly effective capability that can discover and classify sensitive data in your environment, helping to prevent data leakage. But it also has features useful to developers to minimize the exposure of confidential customer information when handling large volumes of sensitive data. By taking advantage of Sensitive Data Protection transformation techniques, you can de-identify sensitive information in a dataset through redaction, replacement, masking, tokenization, bucketing, date shifting, and time extraction. Developers retain the ability to test applications using functional data while still meeting security requirements put in place to protect customer information. By using pseudonymization, which is reversible and provides an easier path for troubleshooting, developers will have a more useful dataset for functional testing than they would if they used data anonymization. In this talk, you’ll learn how to use the Cloud Data Loss Prevention API (DLP API) of Sensitive Data Protection to inspect data for sensitive information and build an automated data transformation pipeline to create de-identified copies of your dataset.