The objective of this project is to improve understanding of how synthetic data generators, as a privacy enhancing technology, work with large real-world data (RWD) (e.g., datasets with over 30 billion rows of data) in a secure super compute environment. Synthetic data are developed through statistical or machine learning techniques that result in a dataset that contains new records with similar aggregate statistical properties as the original dataset while protecting privacy and confidentiality. Synthetic data also provide a tiered access approach to restricted data that may be more difficult to access otherwise. This work will inform the development of a synthetic data generator toolkit that will include but not be limited to methods to assess privacy risk, data utility and open-source AI methods to generate synthetic data.