Modern AI depends on unstructured text, but sensitive information embedded in that text creates one of the biggest barriers to safe AI adoption. Names, dates, identifiers, and quasi-identifiers are highly contextual, inconsistently formatted, and difficult to detect reliably at scale. This whitepaper examines why traditional rule-based systems, general-purpose NER models, and cloud APIs often fall short when accuracy, recall, and compliance truly matter.
In this report, we benchmark Tonic Textual against widely used open-source frameworks and commercial services including AWS Comprehend, Azure AI Language, Google Cloud Sensitive Data Protection, Microsoft Presidio, GLiNER, and spaCy. Using industry-specific datasets spanning legal documents, electronic health records, and customer service transcripts, we present precision, recall, and F1 results under apples-to-apples conditions. The findings highlight what it takes to achieve compliance-grade sensitive text identification without sacrificing data utility, and why getting this layer right unlocks safer, more scalable AI development.
