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System Design Masterclass · Data Governance Compliance
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The obvious PII: names, emails, social security numbers, credit card numbers. Every engineer knows to protect these.
The non-obvious PII: IP addresses (GDPR considers them personal data), device IDs, location data, cookie identifiers, biometric data, genetic data, browsing history, search queries, and even combinations of non-PII that become identifying together. Latanya Sweeney's research at Harvard showed that zip code + birth date + gender identifies 87% of Americans uniquely. Three "harmless" fields, combined, become a fingerprint.
A company stores "anonymous analytics" with user agent strings, screen resolution, timezone, and installed fonts. The Electronic Frontier Foundation's Panopticlick project showed that this combination creates a unique browser fingerprint for 94% of browsers. That "anonymous" analytics data is PII.
PII handling is the practice of identifying all personal data in your systems, classifying it by sensitivity, applying appropriate protections, and managing its lifecycle from collection to deletion. It's not just about the database. PII hides in logs, caches, backups, error messages, analytics events, email systems, support tickets, and third-party integrations.
Uber learned this the hard way in 2016. Attackers accessed a private GitHub repository that contained AWS credentials, which led to an S3 bucket holding the names and driver's license numbers of 600,000 drivers and the personal information of 57 million users. PII in a place nobody expected: a backup stored in a cloud bucket accessed through credentials in a code repository.
| Category | Examples | Protection Level |
|---|---|---|
| Direct identifiers | Name, email, SSN, passport number, phone | High: encrypt, mask, access-control |
| Indirect identifiers | IP address, device ID, cookie ID, employee ID | Medium: pseudonymize, limit retention |
| Quasi-identifiers | Age, gender, zip code, job title | Medium: generalize when sharing, anonymize for research |
| Sensitive PII | Health data, biometrics, political opinions, sexual orientation, religious beliefs | Very high: field-level encryption, explicit consent, separate storage |
| Financial PII | Credit card, bank account, tax ID | Very high: PCI DSS compliance, tokenization |
PII discovery: find it before it finds trouble.
Step 1: Audit every data store (databases, caches, logs, file storage, third-party systems). Step 2: Scan for PII patterns (email regex, SSN patterns, credit card Luhn check, name entity recognition). Step 3: Classify each field by PII category and sensitivity level. Step 4: Document in a data inventory: what PII, where, why it's collected, who has access, retention period, legal basis for processing.
AWS Macie scans S3 buckets and reports PII it finds, including data you didn't know was there. Google Cloud DLP does the same across BigQuery, Cloud Storage, and Datastore. Microsoft Purview covers Azure , Blob Storage, and even on-premises file shares. These tools catch the PII you didn't know you were storing, the backup files, the exported CSVs, the debug dumps.