102 AI PII Anonymization Pain Points

Every NER model, regex pattern, and ML classifier produces confidence scores, not certainties. 10 pain points per category across the full AI anonymization stack.

This research track documents 100 pain points generated by 7 structural drivers of AI-based PII anonymization failure, including statistical irreducibility barriers, context boundary failures, adversarial attack vulnerabilities, and compliance indeterminacy challenges. The analysis covers NLP-based detection, computer vision, and audio processing systems across multiple deployment contexts. This track is one of 14 in the anonym.community corpus documenting 1,478 total pain points and 98 structural drivers. The full analysis including product case studies, driver mechanisms, and implementation guidance is available at the anonym.community research dashboard, which covers 240 jurisdictions and 140 product case studies.