Model-agnostic PII detection with LLMs | Amazon Web Services
A configurable, instruction-driven detector that runs on any large language model (LLM) managed on Amazon Bedrock, evaluated on five public PII corpora across nine LLM-based detectors, including the OpenAI PrivacyFilter.
Fine-tuning a model on real-world text creates a personally identifiable information (PII) detection problem. Training corpora are full of PII: names, home addresses, email and phone numbers, national-ID and social-security numbers, bank accounts, dates of birth. A model trained on uncleaned text can memorize that data and later reproduce it, leaking a real person’s details through a prompt that was never meant to surface them. In this post, we describe a configurable, model-agnostic detector built on large language models (LLMs), walk through its implementation, benchmark it against an off-the-shelf tool, and show how to run it on your own data.
Sample code: The detector described in this post ships as the pii-detector package, available in the sample-llm-pii-detection repository. Every...
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