Python PII Scanner Validates Matches to Cut False Positives in Data Discovery

Knowing where personal data sits is a prerequisite for encrypting, masking, or deleting it, and regulations such as GDPR Article 30 require a record of the categories processed. A plain regex scan tends to flag harmless values like order numbers, timestamps, and SKUs, which makes its reports hard to use. The article presents a Python scanner that samples each column with reservoir sampling, uses regexes to find candidates, and then validates them with checksums and format rules such as Luhn, mod-97 for IBANs, and SSA rules for SSNs. It combines validation results with the column name to assign a per-column confidence score and writes an inventory in JSON and Markdown. The author tests the approach on a synthetic SQLite database and CSV files, and compares the results with Microsoft Presidio.