The role of synthetic data in governance: Frameworks for ethical implementation and regulatory compliance

Synthetic data has emerged as a transformative resource in artificial intelligence development, offering compelling solutions to longstanding challenges in data privacy, accessibility, and representational equity. This article examines the governance dimensions of synthetic data deployment, with particular attention to emerging risks including algorithmically hallucinated content, unintentional privacy leakages, and potential regulatory circumvention. Despite significant adoption growth across regulated industries, substantial governance gaps persist, with many organizations lacking formal frameworks, quality assessment protocols, and documentation standards specific to synthetic data. The regulatory landscape remains largely underdeveloped, creating compliance uncertainty for implementing organizations. To address these challenges, this article introduces two novel frameworks: the Synthetic Data Governance Checklist (SDGC) and Synthetic Integrity Index (SII). These complementary tools enable systematic evaluation of synthetic dataset fitness, privacy guarantees, and ethical implications across deployment contexts. Validation testing demonstrates significant reductions in governance vulnerabilities, compliance incidents, and privacy risks following implementation, positioning these frameworks as essential components for responsible synthetic data deployment in high-stakes domains.