Beyond Real Data: Grounded Synthetic Intelligence for Trustworthy and Personalised AI

Artificial intelligence is entering a post-data era in which access to sensitive data, rather than its availability, has become the primary constraint on progress. Regulatory frameworks, privacy protections, and governance requirements increasingly limit the direct use of real-world datasets, particularly in domains such as digital health and personalised human-centric systems. This position paper argues that synthetic data should be recognised as foundational infrastructure for computational intelligence operating under such constraints. We introduce the concept of Grounded Synthetic Intelligence (GSI) , a paradigm in which synthetic data functions as a scalable extension of real data while remaining anchored to authentic observations through hybrid training and validation strategies. Rather than treating synthetic data as a replacement for real data, the GSI framework emphasises disciplined integration, rigorous evaluation, and governance-aware deployment to ensure both utility and trustworthiness. Empirical insights from behavioural and health data workflows illustrate how synthetic augmentation can improve learning when datasets are limited or sensitive, while also demonstrating the risks of over reliance and distributional drift when synthetic data is used in isolation. The paper identifies key challenges including fidelity–utility tradeoffs, domain dependence, and the need for standardised evaluation protocols, and outlines future research directions for trustworthy generation, domain-aware modelling, and integration with personalised AI systems. By reframing synthetic data as an enabling infrastructure rather than a convenience technique, this work aims to guide the development of secure, privacy-preserving, and clinically useful artificial intelligence systems in increasingly regulated environments.