How is Natural Language Processing (NLP) used to detect PII in unstructured text?

Named Entity Recognition (NER) automatically finds and classifies entities (names, locations, dates), and context-aware masking decides what to transform while preserving data utility.

• Named Entity Recognition (NER): deep-learning NLP models scan text and tag entities — names, locations, organizations, dates, and custom PII types — with increasing accuracy. Capabilities include multi-language support, context-aware detection, custom entity types, and relationship extraction.
• Context-aware masking: rather than blindly redacting every detected item, these systems understand which elements are essential for analysis and apply the appropriate transformation — keeping the data useful.

Why NER beats regex: regular expressions catch fixed patterns (credit-card numbers, emails) but miss free-form references ("the CEO's daughter who moved to Bern last spring"). NER understands language and context.

Go deeper:

• Named-entity recognition (Wikipedia) — how NER locates and classifies entities in unstructured text.