# Introduction The field of Artificial Intelligence is rapidly evolving and widely uses synthetic data to accelerate innovation while preserving privacy and enabling broader data accessibility. However, the evaluation of synthetic data remains fragmented across heterogeneous metrics, ad-hoc scripts, and incomplete reporting practices. ## Key Features The framework supports several aspects: * Automated feature-type detection * Distributional and dependency-level fidelity metrics * Graph- and embedding-based structure preservation scores * A rich suite of data visualization schemas ## Demonstration of Value SDB was evaluated in three real-world use cases that differ substantially in scale, feature composition, statistical complexity, and downstream analytical requirements. These include: * Healthcare diagnostics * Socioeconomic and financial modeling * Cybersecurity and network traffic analysis These use cases demonstrate how SDB can address diverse data fidelity assessment challenges, varying from mixed-type clinical variables to high-cardinality categorical attributes and high-dimensional telemetry signals, while offering a consistent, transparent, and reproducible benchmarking across heterogeneous domains.