Data Anonymization for LLMs

Protecting user privacy while training advanced AI models is a core challenge we solve through state-of-the-art anonymization techniques.

1. Automated PII Scrubbing

We use specialized NLP models to identify and remove Personally Identifiable Information (PII) such as names, emails, and phone numbers from training data before it ever reaches our LLMs.

2. Differential Privacy

We inject mathematical noise into our training algorithms to ensure that the model learns general patterns without memorizing specific details about any individual user.

3. Synthetic Data Generation

Where possible, we use high-quality synthetic data to augment our training sets, reducing the reliance on raw user data.

4. Zero-Knowledge Training

Our infrastructure is designed so that training processes operate on encrypted or anonymized shards, ensuring that no single component has access to full, identifiable records.