Synthetic Data Compliance Framework (SDCF): A Purpose-Bounded Methodology for Assessing Synthetic Data Privacy, Fidelity, and Fairness

Synthetic data is increasingly adopted for privacy-preserving analytics, data sharing, and AI model development in regulated environments. However, organisations lack standardised methodologies for determining whether synthetic datasets satisfy regulatory expectations for privacy protection, statistical utility, and fairness. This paper presents the Synthetic Data Compliance Framework (SDCF), a purpose-bounded assessment methodology connecting quantitative metrics to regulatory requirements under GDPR, the EU AI Act, and relevant ISO/IEC and NIST standards. SDCF introduces a tiered assessment architecture (Gold, Silver, Bronze) based on source data accessibility, with composite metrics for Privacy Risk Score (PRS), Fidelity Index (FI), and Fairness Variance (FV). Preliminary empirical evaluation of the Bronze Tier methodology across ten heterogeneous synthetic datasets demonstrates conservative risk classification (60% Restricted outcome), meaningful quality discrimination (B-PRS range: 9 to 79), and cross-domain applicability.