What Is Synthetic Data? — How AI Trains When Real Data Is Scarce or Restricted

Training a good AI model requires large amounts of data. But in many of the domains where AI would be most valuable — healthcare, finance, legal services, maritime operations — the data is sensitive, restricted by regulation, or simply not available in sufficient quantities. Synthetic data is one of the key solutions to this problem.

What is synthetic data?

Synthetic data is artificially generated data that is designed to have the same statistical properties and patterns as real data — without containing any actual real-world records. It is not collected from real people or real events. It is created by a model that has learned the structure and patterns of the real data and can generate new examples that are statistically similar but do not correspond to any real individual or event.

A simple example

A hospital wants to train an AI model to detect a rare condition from patient records. The condition affects only a small number of patients, so the real dataset is too small to train a good model. Privacy regulations prevent the data from being shared outside the hospital. Synthetic data solves both problems: generate thousands of synthetic patient records that have the same statistical characteristics as the real records — the same age distributions, the same symptom patterns, the same lab value ranges — without corresponding to any real patient. The AI trains on the synthetic data and learns to detect the condition.

Why synthetic data is valuable

Privacy — synthetic data contains no real personal information. It can be shared, published, and used across organisations without privacy concerns. Scale — real data is limited by what has actually happened. Synthetic data can be generated at any scale needed, including for rare scenarios that do not occur frequently enough in the real world to provide adequate training examples. Cost — collecting, cleaning, and labelling real data is expensive. Generating synthetic data at scale is far cheaper. Rare event coverage — some events are so rare that real datasets contain almost no examples. Synthetic data can generate thousands of examples of rare scenarios — a critical feature for training models that need to handle edge cases reliably.

Where synthetic data is used

Medical AI — generating synthetic patient records and medical images to train diagnostic models where real data is privacy-restricted. Autonomous vehicles — generating synthetic driving scenarios including rare and dangerous situations that cannot safely be reproduced in real life. Financial services — generating synthetic transaction data to train fraud detection models. Software testing — generating synthetic datasets that resemble production data for testing and development. AI training at scale — major AI labs now use synthetic data generated by existing models to train new, more capable models.

The limitation to know

Synthetic data is only as good as the model that generates it. If the generative model has not accurately learned the structure of the real data, the synthetic data will not be a faithful representation — and training on it may produce a model that performs well on synthetic examples but poorly on real ones. Validation against real data is always necessary.

The simple rule

Real data: scarce, sensitive, expensive, limited to what actually happened. Synthetic data: generated at scale, privacy-safe, can cover rare scenarios, as good as the model that creates it. When real data is unavailable or insufficient, synthetic data bridges the gap.

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