Privacy AI

Creating PII-focused LLMs

Our PII-focused models are built through a process of precision fine-tuning. We move beyond general knowledge by training our AI models on datasets of sensitive information teaching it to recognize everything from bank account numbers to complex medical codes. The result is an optimized AI model that can deliver reliable accuracy.

Data Curation and Generation

We begin by assembling high-fidelity datasets that represent a vast array of global PII and PHI entities. By sourcing diverse examples of sensitive data ranging from international tax identifiers to medical terminology we ensure the model is exposed to the true complexity of real-world documentation before training ever begins.

To ensure privacy, our models may be trained using high-fidelity synthetic data. We can generate millions of realistic but fake data scenarios ranging from medical records to financial statements that mimic the complexity of authentic documents. This allows the AI model to learn PII entities without exposing the model to actual sensitive information during the training process.

Contextual fine-tuning

Our models are trained to identify sensitive entities based on the words surrounding them. This stage moves the AI beyond simple pattern recognition, allowing it to have an understanding of linguistic intent and distinguish between sensitive information and harmless data that shares a similar structure.

Benchmarking

Every model is put through a gold standard evaluation using Philter Scope. We test the AI against annotated datasets to measure its precision and recall to gain an understanding of the model's potential performance when deployed.

Performance Optimization

Finally, we apply compression techniques like quantization to shrink the model without inhibiting its ability to identify PII. This optimizes the AI model and often allows it to achieve enterprise-level performance without the need for expensive GPU clusters.