PII Detection (Personal Identifiable Information) is the automated process of identifying and optionally redacting sensitive personal data in text — such as names, addresses, phone numbers, social security numbers, email addresses, and financial information. It is essential for data privacy, regulatory compliance, and AI safety.

Types of PII Detected

• Direct Identifiers: Full names, Social Security numbers, passport numbers, driver's license numbers — data that uniquely identifies a person.
• Contact Information: Email addresses, phone numbers, physical addresses, IP addresses.
• Financial Data: Credit card numbers, bank account numbers, financial records.
• Health Information: Medical record numbers, diagnoses, treatment details (protected under HIPAA in the US).
• Biometric Data: Fingerprints, facial recognition data, voiceprints.
• Quasi-Identifiers: Combinations of data (zip code + birth date + gender) that can re-identify individuals.

Detection Methods

• Pattern Matching: Regular expressions for structured PII like phone numbers (\d{3}-\d{3}-\d{4}), SSNs, credit card numbers, and email addresses.
• NER (Named Entity Recognition): ML models trained to identify names, locations, organizations, and other entity types in unstructured text.
• Specialized PII Models: Purpose-built models like Microsoft Presidio, AWS Comprehend PII, and Google DLP that combine pattern matching with ML for comprehensive detection.
• LLM-Based: Prompt large language models to identify and classify PII, useful for complex or contextual cases.

Actions After Detection

• Redaction: Replace PII with placeholder text (e.g., "[NAME]", "[EMAIL]", "*--1234").
• Masking: Partially obscure PII while preserving format.
• Tokenization: Replace PII with reversible tokens for authorized de-identification.
• Alerting: Flag documents containing PII for human review.

Regulatory Drivers

PII detection is mandated by GDPR (EU), CCPA (California), HIPAA (US healthcare), and many other privacy regulations. Failure to protect PII can result in significant fines and reputational damage.