Behind many of the artificial intelligence systems now in everyday use sits a quieter ingredient than the famous troves of internet text and images: data that no human ever produced and no sensor ever recorded. Synthetic data is information generated by an algorithm rather than collected from the real world, and it has moved from a niche workaround to a mainstream tool for training, testing, and stress-testing machine learning models. It promises to sidestep privacy law, fill gaps where real records are scarce or dangerous to gather, and manufacture the rare events a model would otherwise almost never see. But the same techniques carry documented failure modes, from models that degrade when fed their own output to the mistaken belief that anything artificial is automatically anonymous. This article explains how synthetic data is made, where the evidence shows it genuinely helps, and where it quietly breaks. It is a general technology explainer, not legal, medical, or financial advice.
What Synthetic Data Actually Is
Synthetic data is data created by a computational process designed to resemble real data without being a direct copy of it. It can take any form that real data does: tabular records such as patient charts or bank transactions, images, video, audio, sensor streams, or free text. The defining feature is provenance. A real dataset records something that happened; a synthetic dataset is sampled from a model or a simulation that is meant to behave like the thing it imitates.
That distinction matters because the goal is rarely to reproduce individual records. It is to preserve the statistical structure of a population, the correlations, distributions, and patterns, so that a model trained on the artificial version learns roughly what it would have learned from the original. National standards bodies treat the technique seriously enough to formalize it. The United States National Institute of Standards and Technology lists generating synthetic data using models as one of the recognized techniques for de-identifying government datasets, alongside removing identifiers and transforming quasi-identifiers [1].
How It Is Generated
There are two broad families of methods, and many real systems combine them.
- Simulation, also called procedural or physics-based generation, builds data from explicit rules and engines. A driving simulator renders a street scene with controllable weather, lighting, pedestrians, and traffic; a financial simulator generates transaction streams from defined behavioral rules. The data is synthetic because a model of the world produced it.
- Generative models learn the distribution of a real dataset and then sample new examples from it. Generative adversarial networks, which pit a generator against a discriminator, are a widely used approach for synthesizing tabular clinical information, and they are now joined by diffusion models, variational autoencoders, and large language models for text [2].
- Statistical oversampling sits between the two. The Synthetic Minority Over-sampling Technique, or SMOTE, manufactures new minority-class examples by interpolating between existing ones, a simple but durable method for rebalancing skewed datasets [3].
The choice depends on the data type and the goal. Simulation excels when the rules of a domain are well understood and rare scenarios must be authored on demand. Generative models excel when the patterns are too complex to write by hand and a representative real dataset already exists to learn from.
Why Organizations Use It
Four motivations recur across the research and industry literature, and they often overlap.
- Privacy. Real records about people are governed by laws such as HIPAA and the GDPR. Synthetic data offers a way to share something statistically useful without releasing the underlying individuals, which is why NIST situates it within de-identification practice [1].
- Scarcity and cost. Labeled real data can be expensive, slow, or impossible to gather at scale. Where collection is impractical, a generator can produce large volumes cheaply.
- Edge cases. The events a safety-critical model most needs to handle are often the ones that appear least in real logs. Synthetic generation lets engineers author those situations deliberately rather than wait for them.
- Balancing datasets. When one class dominates, such as legitimate transactions vastly outnumbering fraudulent ones, models tend to ignore the rare class. Synthetic examples of the minority class push the model to pay attention to it [3].
Where It Genuinely Helps
The strongest case for synthetic data appears where real data is rare, sensitive, or hazardous to collect, and three domains illustrate the pattern.
In autonomous driving, the most dangerous situations are statistically vanishing. A pedestrian stepping off a curb into traffic, debris on the road during a storm, or blinding sun glare may represent a tiny fraction of logged miles, yet a self-driving system must handle all of them. Simulation lets developers generate these scenarios at scale and with precise control. Waymo, for example, has described training and testing its system across vast volumes of simulated driving that would be impractical to accumulate on public roads alone [4].
In healthcare, patient data is both scarce for rare conditions and tightly protected. Generative models have been applied to expand limited clinical datasets and enable research that the original records could not support, with generative adversarial networks especially common for tabular clinical data [2]. There is also evidence that carefully designed synthetic generation can improve fairness by boosting the representation of underrepresented patient groups [5].
In fraud and financial crime, the core problem is imbalance: genuine fraud is rare relative to normal activity, so a naive model learns to label almost everything legitimate. Generating synthetic examples of fraudulent behavior, through SMOTE and its many hybrid variants, has been shown to improve a model's ability to catch the minority class it would otherwise miss [3].
The Documented Pitfalls
The most striking risk is what researchers call model collapse. In a 2024 study published in Nature, Shumailov and colleagues showed that when generative models are trained recursively on data produced by earlier generations of models, they degrade. The first thing lost is the tails of the distribution, the rare and unusual cases, in a phase the authors term early model collapse. Over successive generations the distribution narrows further until it bears little resemblance to the original, late model collapse, and the model effectively forgets the real world it was meant to represent [6]. As AI-generated content fills the internet, this raises a genuine concern that future models could be trained on the polluted output of earlier ones.
Bias is the second documented hazard. A generator learns whatever is in its training data, including the imbalances. If a group is underrepresented in the source, the synthetic version can reproduce or even worsen that gap. Research on what one team calls fairness feedback loops found that chains of generative models can converge toward the majority and amplify errors, widening the representational gap between groups rather than closing it [7]. The same technique that can improve fairness when applied deliberately can entrench unfairness when applied carelessly.
The third pitfall is the fidelity and validation gap. Synthetic data that looks plausible may not preserve the subtle correlations a downstream model relies on, and a model that performs well on synthetic test data can fail on real inputs. This is why synthetic data cannot be trusted on appearance alone and must be measured.
How Quality Is Evaluated
Researchers generally assess synthetic data along several axes rather than a single score, because a dataset can be faithful yet useless, or useful yet unfaithful.
- Fidelity asks whether the synthetic data statistically resembles the real data. Validation studies have compared utility metrics across many datasets and generation methods, finding measures such as a multivariate Hellinger distance useful for ranking how well a generator preserves the original distribution [8].
- Utility asks the practical question: does a model trained on the synthetic data perform comparably to one trained on real data when tested against reality? This train-on-synthetic, test-on-real check is a standard benchmark.
- Fairness asks whether the data treats subgroups equitably, using metrics tied to demographic parity or equalized odds [5].
For images, fidelity is often judged with established scores such as the Frechet Inception Distance, which compares the statistical properties of generated and real images. No single metric is sufficient, and credible evaluations report several.
The Privacy Nuance: Synthetic Is Not Automatically Anonymous
A persistent misconception is that because synthetic records are not real people, they carry no privacy risk. The evidence contradicts this. Privacy regulators, including the Office of the Privacy Commissioner of Canada, have cautioned that synthetic data must still be assessed for re-identification risk rather than assumed safe [9].
The mechanism is straightforward. A generator that overfits its training data can memorize and effectively leak individual records. Membership inference attacks attempt to determine whether a particular person was in the data used to build a generator, and research has shown that such attacks can succeed against synthetic data using only the released synthetic dataset, without access to the auxiliary real data once assumed necessary [10]. This is why NIST and the broader privacy community pair synthetic generation with formal guarantees such as differential privacy, which adds calibrated noise to bound how much any single individual can influence the output. NIST finalized guidelines in 2025 for evaluating those differential privacy guarantees, while emphasizing that there is no simple answer for balancing privacy against usefulness [11]. Synthetic data can be made strongly private, but only by design and measurement, not by definition.
The Bottom Line
Synthetic data is neither a gimmick nor a cure-all. Used where real data is genuinely scarce, sensitive, or skewed, and validated against reality, it is a legitimate and increasingly standard engineering tool, well-suited to authoring rare driving scenarios, expanding protected health datasets, and surfacing rare fraud. Used carelessly, it invites three concrete failures the literature has now documented: model collapse when systems feed on their own output, bias amplification when generators inherit and magnify imbalances, and a false sense of anonymity when artificial data is assumed to be private without testing. The practical takeaway is that synthetic data earns trust through measurement, not provenance. Faithful, useful, fair, and privacy-tested are properties to be demonstrated for each dataset, not assumed because the records were made by a machine.
Sources
[1] NIST: Special Publication 800-188, De-Identifying Government Datasets: Techniques and Governance — https://csrc.nist.gov/pubs/sp/800/188/final
[2] JMIR / PMC: Utility Metrics for Evaluating Synthetic Health Data Generation Methods (Validation Study) — https://pmc.ncbi.nlm.nih.gov/articles/PMC9030990/
[3] International Journal of Financial Studies (MDPI): Enhancing Financial Fraud Detection through Addressing Class Imbalance Using Hybrid SMOTE-GAN Techniques — https://www.mdpi.com/2227-7072/11/3/110
[4] Waymo: Simulation City, Waymo's most advanced simulation system for autonomous driving — https://waymo.com/blog/2021/07/simulation-city/
[5] PLOS Computational Biology: Generative AI mitigates representation bias and improves model fairness through synthetic health data — https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013080
[6] Nature: AI models collapse when trained on recursively generated data (Shumailov et al., 2024) — https://www.nature.com/articles/s41586-024-07566-y
[7] ACM FAccT 2024: Fairness Feedback Loops, Training on Synthetic Data Amplifies Bias — https://facctconference.org/static/papers24/facct24-144.pdf
[8] JMIR / PMC: Utility Metrics for Evaluating Synthetic Health Data Generation Methods (multivariate Hellinger distance) — https://pmc.ncbi.nlm.nih.gov/articles/PMC9030990/
[9] Office of the Privacy Commissioner of Canada: The reality of synthetic data — https://www.priv.gc.ca/en/blog/20221012/
[10] arXiv (DPM/ESORICS 2023): Synthetic is all you need, removing the auxiliary data assumption for membership inference attacks against synthetic data — https://arxiv.org/abs/2307.01701
[11] NIST: NIST Finalizes Guidelines for Evaluating Differential Privacy Guarantees (SP 800-226) — https://www.nist.gov/news-events/news/2025/03/nist-finalizes-guidelines-evaluating-differential-privacy-guarantees-de
