The effectiveness of data redaction pipelines is fundamentally limited by the accuracy of the initial detection of private or identifiable information. Even manual de-identification processes have struggled to meet buyer standards, and automated tools, like BERT NER models, often catch less than two-thirds of names, places, and organizations. The complexity of indirect identifiers, which only reveal identity in combination, means that models can still re-identify individuals even after direct identifiers are removed. Achieving robust detection requires in-house training, domain-specific ontologies, and a careful balance to avoid false positives.
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