Synthetic data is increasingly used to accelerate AI development, protect sensitive information, and overcome data scarcity. However, poorly governed synthetic data can introduce hidden bias, privacy leakage, and regulatory risk that undermine its intended benefits.

Governance for Synthetic Data is a technical yet practical guide for security, data, and AI leaders responsible for approving, deploying, and overseeing synthetic data initiatives. The book explains when synthetic data is appropriate, when it is not, and what governance controls are required to use it responsibly.

This volume addresses synthetic data through a risk, privacy, and transparency lens, translating emerging regulatory expectations into operational decision-making. It focuses on real-world use cases rather than experimental theory.

Key areas covered include:

• Common synthetic data generation methods and trade-offs


• Privacy and re-identification risks in synthetic datasets


• Bias amplification and fairness considerations


• Legal and regulatory implications of synthetic data use


• Validation techniques to assess utility and risk


• Governance frameworks for approving and monitoring usage

Designed for organizations operating in regulated or high-risk environments, this book provides decision-makers with clear criteria, controls, and oversight practices to ensure synthetic data strengthens AI programs without introducing unmanaged risk.