Build Cloud DLP inspection template configurations with info types, likelihood thresholds, and exclusion rules.
Last verified: May 2026
Output will appear here...Your team is building a customer support tool that ingests user-submitted text. Compliance requires detecting and redacting PII before storing. The builder generates: an inspection template detecting US/EU PII (SSN, credit-card, email, phone, name, IP-address) with VERY_LIKELY threshold, exclusion rules for the team's test data patterns, integration with a deidentification template that replaces matches with redaction tokens. Pipeline: incoming text → DLP inspect → DLP deidentify → store sanitized version. PII never lands in storage; compliance team is happy.
Build Cloud DLP inspection template configurations with info types, likelihood thresholds, and exclusion rules. This tool helps GCP engineers generate valid configurations quickly without consulting documentation, reducing errors and accelerating infrastructure deployment. All processing runs in your browser with no data sent to external servers.
The builder constructs Cloud DLP inspection template configurations: template resource (display name, parent: organization or project), info types (built-in detectors and/or custom info type definitions), minimum likelihood threshold, custom rules (exclusion rules with word lists or regex), include_quote setting (return matched strings or just metadata), and limits (max findings per request). Output is generated as gcloud dlp commands and Terraform google_data_loss_prevention_inspect_template resources.
Cloud DLP has 130+ built-in info type detectors (SSN, credit card, email, phone, IP address, plus jurisdiction-specific like UK NHS number, Australia TFN, etc.). Don't write custom regex; use the built-in detectors. They're tuned for high precision and continuously improved.
Likelihood thresholds matter for false-positive rates. LIKELIHOOD_VERY_LIKELY catches genuine matches with low FP rate; LIKELIHOOD_POSSIBLE catches more but generates false positives on innocuous data (e.g., a 16-digit number that isn't really a credit card). Default to VERY_LIKELY unless your use case justifies more sensitivity.
Use exclusion rules to suppress known-good matches. Test data in dev environments often contains 'fake' SSNs that match the SSN detector — exclude these via regex or word list rules. Otherwise, dev environment scans drown in false-positive noise.
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Disclaimer: This tool runs entirely in your browser. No data is sent to our servers. Always verify outputs before using them in production. AWS, Azure, and GCP are trademarks of their respective owners.