Synthetic data acts as a “hidden lever” in responsible AI by enabling organizations to train, test, and validate AI models without violating privacy, using copyrighted material, or relying on biased real-world datasets. It allows for the deliberate creation of diverse, balanced datasets, transforming AI development from reactive bias correction to proactive “fairness by design“.
In a recent analysis, Professor Peter Lee of UC Davis School of Law argues that synthetic data could reshape the legal and economic landscape of AI. For organizations navigating compliance, intellectual property risks, and data privacy obligations, this development deserves close attention. Synthetic datasets promise to reduce reliance on sensitive real-world information, potentially lowering exposure to copyright disputes and privacy liabilities. For executives responsible for innovative budgets and risk management, that sounds like a compelling proposition.
Yet the opportunity comes with tradeoffs. Synthetic data does not eliminate risk — it transforms it. Lee highlights issues such as hidden bias, model degradation, and governance challenges when artificial datasets begin influencing real-world decision making. In other words, the question for leadership is not whether to adopt AI tools, but how to ensure that the data behind them remains trustworthy and aligned with organizational values.
An international perspective reinforces the stakes. A recent United Nations Institute for Disarmament Research report frames synthetic data as a global governance issue, touching on security, regulatory coordination, and cross-border standards. As AI development becomes increasingly global, corporate leaders may find that synthetic data practices influence everything from vendor selection to compliance frameworks and reputational risk.
So, what does this mean at the executive level?
- Strategy: Synthetic data could accelerate AI deployment while changing how organizations manage intellectual property and data ownership.
- Risk Management: Governance models must evolve to address transparency, bias, and accountability in AI generated datasets.
- Leadership: Boards and executives may need clearer oversight mechanisms to understand how AI tools are trained, not just how they perform.
For many organizations, the next competitive advantage will not simply be adopting AI faster. It will be adopting AI more responsibly. Synthetic data represents a turning point where technology strategy, legal risk, and corporate governance converge — and where informed leadership can make the difference between sustainable innovation and unintended exposure.