Data redaction is the process of automatically detecting and removing sensitive information from text, documents, and datasets — using NLP and pattern matching to identify PII (personally identifiable information), credentials, financial data, and protected health information before sharing, storing, or processing data.

What Gets Redacted

• PII: Names, addresses, phone numbers, email addresses, SSNs.
• Financial: Credit card numbers, bank accounts, salary data.
• Health (PHI): Medical records, diagnoses, treatment information.
• Credentials: API keys, passwords, tokens, connection strings.
• Legal: Attorney-client privileged communications.

Redaction Methods

• Pattern Matching: Regex for structured data (SSN: XXX-XX-XXXX).
• NER (Named Entity Recognition): ML models detect names, locations, organizations.
• LLM-Based: Use language models to identify contextual sensitive information.
• Tokenization: Replace sensitive values with non-reversible tokens.

Tools: Microsoft Presidio, AWS Comprehend PII, Google DLP API, spaCy NER.

Data redaction is essential for privacy compliance — enabling organizations to share and process data safely while meeting GDPR, HIPAA, and CCPA requirements.