Introduction

This section explains why organizations use Synthetic Data and how Protegrity Synthetic Data preserves statistical utility while reducing privacy risk.

Organizations need realistic datasets for model training, testing, and cross-team collaboration without exposing sensitive information. Traditional methods such as masking and redaction can reduce data utility for analytics and machine learning.

Protegrity Synthetic Data learns patterns and distributions from source data. It then generates new records that reflect these patterns without reproducing the original personal records.

Synthetic data enables safer data sharing, faster development and validation, and compliance with regulations such as GDPR and HIPAA.