Best practices for handling PII and sensitive data in AI training pipelines?

All answers to this question.

You should look into "Microsoft Presidio" or "Presidio-Analyzer." It’s an open-source framework that uses a combination of Regex, Logic, and NER (Named Entity Recognition) models to identify and redact PII. In a recent 2024 project, we used it to process over two million legal PDFs. The trick is to replace names and addresses with "tokens" (e.g., [PERSON_1], [CITY_A]) rather than just deleting them. This preserves the syntactic structure and the "who-did-what" relationships, which is vital for the LLM to understand legal context while remaining completely compliant with privacy laws.

   Answered 2024-11-08 by Kimberly Taylor

Have you considered the risk of "Model Inversion" or "Membership Inference" attacks? Even with PII scrubbed, sometimes an LLM can be prompted to reveal sensitive details through context. Are you planning to implement Differential Privacy (DP) during the training phase to add a mathematical layer of protection to your weights?

   Answered 2024-11-10 by Brian Collins

• Brian, that is a high-level concern we are definitely discussing. We are looking at DP-SGD (Differential Privacy Stochastic Gradient Descent) through the Opacus library. It does impact the model's final accuracy slightly, but for legal data, the risk of a "leak" is far more expensive than a 2% drop in performance. We are currently benchmarking the privacy-utility trade-off to see what our legal team is comfortable with.

     Commented 2024-11-12 by Christopher Lee

Another approach is to use a "Synthetic Data" generator to create fake documents that mimic the structure of your real ones, eliminating the PII risk entirely at the source.

   Answered 2024-11-13 by Linda Young

• Synthetic data is a great shout, Linda. Especially for training the base logic, it can drastically reduce the amount of "clean" real-world data you actually need to risk using.

     Commented 2024-11-14 by Kimberly Taylor