Model-agnostic PII detection with LLMs
Amazon Web Services Christophe Dupuy
Model-agnostic PII detection was built as a configurable, instruction-driven LLM detector that can run on any LLM served through Amazon Bedrock or a self-hosted backend. It was benchmarked on five Hugging Face PII corpora containing 49,365 records and 222,114 ground-truth spans. The approach changes from fixed, retrainable token-labeling schemas to prompt-based configuration where adding or removing PII entity categories requires no retraining or redeployment.
Why it matters
A configurable, model-agnostic detector that turns any large language model on Amazon Bedrock into a PII detector. Because the entities to detect live in a prompt rather than in code, one detector adapts to new entity types without retraining, and it outperforms an off-the-shelf tool across five public corpora and nine LLM-based detectors.