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NEWS · RESEARCH · #191

Model-agnostic PII detection with LLMs

According to AWS Machine Learning, they developed a configurable, model-agnostic detector that uses prompts to turn any LLM on Amazon Bedrock into a PII detector; because entity types live in the prompt rather than code, the detector can adapt to new entities without retraining and (AWS reports) outperformed an off-the-shelf tool across five public corpora and nine LLM-based detectors.

KEY POINTS

  1. According to AWS Machine Learning, they developed a configurable, model-agnostic detector that uses prompts to turn any LLM on Amazon Bedrock into a PII detector; because entity types live in the prompt rather than code, the detector can adapt to new entities without retraining and (AWS reports) outperformed an off-the-shelf tool across five public corpora and nine LLM-based detectors.
  2. This matters because a prompt-configured, model-agnostic PII detector could simplify deployment and faster adaptation of privacy controls for LLMs without costly retraining.
  3. Model-agnostic PII detection with LLMs

WHY IT MATTERS

This matters because a prompt-configured, model-agnostic PII detector could simplify deployment and faster adaptation of privacy controls for LLMs without costly retraining.

SOURCES & TIMELINE

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AWS MACHINE LEARNING FIRST-PARTY
Model-agnostic PII detection with LLMs

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.

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