Privacy

How to measure and minimize personally identifiable information (PII) risk in synthetic data.

Leveraging Gretel's privacy-preserving synthetic data generation platform to fine-tune Azure OpenAI Service models in the financial domain.

Safely leverage sensitive or proprietary text data for advanced language model training and fine-tuning

How we implemented a practical attack on a synthetic data model to validate its ability to protect sensitive information under different parameter settings.

How to safely fine-tune LLMs on sensitive medical text for healthcare AI applications using Gretel and Amazon Bedrock

Gretel releases a new synthetic financial document dataset to empower AI developers in building customized and highly performant sensitive data detection systems.

Create privacy-safe synthetic patient data with Gretel, ensuring compliance, secure sharing, and actionable insights for AI and machine learning in healthcare.

Test data generation is the process of creating new data that replicates an original dataset. Here’s how developers and data engineers use it.

Gretel introduces Reinforcement Learning from Privacy Feedback (RLPF), a novel approach to reduce the likelihood of a language model leaking private information.

Gretel releases industry standard synthetic tabular data privacy evaluation and risk-based scoring system.