SDAIA’s roadmap for synthetic data: Simulating reality does not eliminate identity disclosure risks and statistical bias

Escaping to synthetic data generation appears to be a practical solution for any development team confronting data scarcity or the high costs of collection, yet statistically simulating reality does not exempt one from its legal and technical repercussions. In a new study released by the Saudi Data and Artificial Intelligence Authority (SDAIA) titled “Synthetic Data: Opportunities, Challenges, and Governance Mechanisms,” we are faced with a systematicEscaping to synthetic data generation appears to be a practical solution for any development team confronting data scarcity or the high costs of collection, yet statistically simulating reality does not exempt one from its legal and technical repercussions. In a new study released by the Saudi Data and Artificial Intelligence Authority (SDAIA) titled “Synthetic Data: Opportunities, Challenges, and Governance Mechanisms,” we are faced with a systematic analysis that shows how this technology can open avenues for innovation and efficiently train AI models, while establishing decisive safeguards that prevent slipping into assumptions of absolute safety.

The study, which aligns with designating 2026 as the Year of Artificial Intelligence in the Kingdom and with the objectives of Saudi Vision 2030, showed that synthetic data provide datasets that mimic the statistical properties of real data. Their direct benefit lies in addressing the scarcity of training datasets and reducing the cost of preparing them, as well as facilitating their sharing among institutions that handle sensitive information, particularly in the healthcare and financial services sectors, while respecting privacy protection requirements.

Relying on generated data requires a deep understanding of how closely it is linked to the original records.The study reviewed data creation methodologies across three main types defined by the degree of reliance on real data: fully synthetic data, partially synthetic data, and hybrid synthetic data. The importance of these types goes beyond merely expanding the data available for training models; they also improve the representation of underrepresented classes in conventional datasets, helping to reduce bias and enhance the fairness of intelligent system outputs.

However, the core warning in the “SDAIA” document is that it breaks the prevailing impression that generation automatically eliminates risks. The report stresses that simulating real data does not necessarily mean that the risks of re-identifying individuals or of underlying statistical biases disappear.Privacy protection is essentially tied to specialized tests that measure the quality of the simulation and examine the safety of the outputs.Accordingly, the Authority emphasized the need for periodic risk assessments and specialized tests to verify data quality and its suitability for the intended purpose, to achieve the critical balance between privacy protection and the desired benefit.

And to turn these principles into an operational path, the study presented a roadmap for responsibly adopting synthetic data built on five pillars: defining usage objectives, establishing governance, accountability and risk-management frameworks, setting precise standards to verify data quality, applying transparency requirements and source tracking, and leveraging experimental regulatory environments. These environments allow entities to test innovative technical solutions and verify their suitability within controls that protect privacy and build trust before wide release.

What actually changes for those developing AI applications in the Gulf, Egypt, or the Levant after this shift? At the institutional decision level, engineering teams in banks and hospitals are moving toward lowering data-collection costs using synthetic alternatives, but they find themselves required to allocate new budgets for auditing generation assets and quality testing. At the skill level, model engineering alone is no longer sufficient; risk measurement of re-identification and statistical verification that data are free of bias have become indispensable requirements for competing in markets that are moving toward precise and responsible sovereign governance.

Building a national data system requires a clear commitment to statistical integrity standards and source tracing.And with Sadaia cementing its role as a national reference for data and artificial intelligence in the Kingdom, adopting governance mechanisms and early testing within experimental environments becomes a decisive condition to ensure the reliability of intelligent applications and avoid regulatory exposure.