Redact SSN and Credit Card Numbers from Support Ticket Exports
Customer support tickets frequently contain accidentally disclosed sensitive information — customers paste full Social Security Numbers when verifying identity, type complete credit card numbers when disputing charges, or share bank account details when setting up payment plans. Zendesk and Intercom exports preserve these in plaintext within the Description and Comment columns. Before sharing support logs with QA teams, training vendors, or analytics platforms, this PII must be systematically redacted. The challenge: SSN formats vary wildly — 123-45-6789, 123 45 6789, 123456789 — and credit card numbers appear with or without spaces, dashes, or grouping. Simple find-and-replace misses most variants. Regex patterns can catch these reliably, but pasting production support logs containing real SSNs and card numbers into online regex testing tools constitutes a data exposure incident by itself. This workflow applies comprehensive PII detection patterns locally, replacing matches with [REDACTED-SSN] or [REDACTED-PAN] tokens.
Why this matters?
A healthcare SaaS company's support team exported 28,000 Zendesk tickets for a quarterly quality review, intending to share the anonymized dataset with an external NLP vendor for sentiment analysis. A spot check revealed 1,400+ tickets contained patient SSNs pasted during identity verification flows, plus 890 tickets with full credit card numbers from billing disputes. The data governance team blocked the vendor transfer and classified the existing export as an internal security incident. Remediation required retrieving all copies, implementing email retention policies, and conducting mandatory retraining. The company now mandates local regex redaction of all support exports before any external sharing.
The 3-Step Solution
Follow this streamlined workflow to transform your raw data export into a clean, analysis-ready dataset. Each step leverages our browser-based tools to ensure your sensitive data never leaves your device.
By following these three steps, you eliminate manual data wrangling, reduce human error, and maintain full GDPR compliance throughout the process.
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