The Saudi Data and Artificial Intelligence Authority (SDAIA) has established a comprehensive research framework for synthetic data governance, warning against the automatic assumption that such data is free from re-identification risks or statistical bias in sensitive applications.
SDAIA study release and categorization of synthetic data categories
Healthcare and financial applications and the risks of re-identification and bias
The exploratory nature of the recommendations and their importance for the Kingdom’s national ecosystem
Frequently asked questions
SDAIA study release and categorization of synthetic data categories
The Saudi Data and Artificial Intelligence Authority (SDAIA) has published a specialized research study titled “Synthetic Data: Opportunities, Challenges, and Governance Mechanisms.” The Saudi Press Agency (SPA) and Saudi news platforms, including Al-Madina newspaper at 17:23 Saudi time corresponding to 18:23 Gulf Standard Time, and Akhbar 24 at 15:07 Saudi time on October 31, 2026, reported the details of the study and reviewed its recommendations regarding the responsible use of synthetically generated datasets in developing and evaluating artificial intelligence systems.
Synthetic data is defined as computer-generated information aimed at simulating and reproducing useful patterns or statistical properties of real observations and data. While this type of data can sometimes reduce reliance on expensive or sensitive real record collection, its accuracy and scientific utility depend directly on how it is generated and validated. For example, an artificial medical record should not be treated automatically as appropriate evidence for drawing a clinical treatment conclusion.
The scientific study issued by SDAIA distinguishes between three main categories: fully synthetic datasets, partially synthetic data, and hybrid data. These categories reflect the extent to which the generated materials rely on actual data. In practice, a dataset may maintain certain general statistical features while altering or generating individual records, and its usefulness remains tied to the nature of the specific task.
Healthcare and financial applications and the risks of re-identification and bias
SDAIA’s study highlights potential uses for synthetic data in the healthcare and financial services sectors, where access to real records is restricted by stringent requirements to protect privacy, medical confidentiality, and banking secrecy. The study also discusses the possibility of increasing the representation of underrepresented groups in existing datasets, while emphasizing the need to balance this potential against the risk of reproducing inherent biases from original data or introducing artificial relationships that do not exist in the actual population.
The research explicitly warns that synthetic information does not automatically eliminate the risk of re-identifying individuals. A model trained on sensitive records may retain distinct combinations of characteristics that allow individuals’ identities to be deduced or recognized. Similarly, a synthetic dataset can appear logical and convincing on the surface while failing to reproduce the precise relationships important for a scientific or operational task.
The governance roadmap recommended in the study includes precisely defining the intended purpose of use, documenting data origin and assets, assigning responsibilities and accountability, assessing risks, testing data quality, and applying transparency requirements. Regulatory sandboxes were also identified as suitable environments for conducting supervised experiments.
The exploratory nature of the recommendations and their importance for the Kingdom’s national ecosystem
These outputs represent research recommendations and proposed governance guidelines rather than evidence that the Kingdom of Saudi Arabia has enacted a new mandatory law specifically for synthetic data. Furthermore, the publication of the study does not imply that any generated dataset has been officially certified as secure for unrestricted sharing.
SDAIA serves as the primary institutional reference for data and artificial intelligence in the Kingdom of Saudi Arabia, making this study directly relevant to national governance and technical application development. Its importance extends far beyond major model launch announcements, given that the availability of reliable training and evaluation data represents a governing constraint for public and private sector AI projects.
The significance of the research lies in providing a documented Saudi institutional framework for evaluating synthetic information instead of automatically assuming that computer-generated data is private, neutral, or accurate. Next steps include publicly applied standards, technical testing methods, sectoral guidelines, and measurable outcomes from pilot projects.
Frequently asked questions
Question: What are the three categories of synthetic data identified by the SDAIA study?
Answer: The study distinguished between fully synthetic data, partially synthetic data, and hybrid data, depending on their degree of reliance on real records.
Question: What are the most prominent sectors positioned to benefit from synthetic data in Saudi Arabia?
Answer: The study highlighted the healthcare and financial services sectors, given that access to real data within them is subject to strict privacy protection restrictions.
Question: Do synthetic data completely eliminate the risks of individual privacy exposure?
Answer: No, SDAIA warned that models trained on sensitive records may retain distinct characteristics that allow the re-identification of individuals or produce misleading statistical relationships.
Question: What are the elements of the governance roadmap recommended by SDAIA?
Answer: They include defining the purpose of use, documenting data sources, assigning responsibilities, assessing risks, testing quality, enhancing transparency, and utilizing regulatory sandboxes.
