What Is Synthetic Data and When Should You Use It?

Your developers need realistic customer data to test a new checkout flow, but copying production records into a test environment is a privacy and security risk. Your data science team wants to train a fraud model, but fraud cases make up a tiny fraction of transactions. A research partner wants to study your data, but you can’t hand over personal records.

Synthetic data is one answer to all 3 problems. It is also easy to misuse. This guide explains what synthetic data is, how it is generated, when it helps and the risks that come with data that looks real but isn’t.

What synthetic data is

Synthetic data is data created by an algorithm rather than collected from real events or people. A good synthetic dataset keeps the useful statistical properties of the real data, such as distributions, correlations and typical ranges, without containing the actual records.

For example, a synthetic customer table might contain 100,000 people with realistic ages, ZIP codes, purchase amounts and churn rates that follow the same patterns as your real customers. But no row corresponds to a real person.

Synthetic data is not new. The US Census Bureau has offered a synthetic data product, the SIPP Synthetic Beta, which links survey responses with tax and benefit records and replaces sensitive values with draws from statistical models so outside researchers can work with it. What has changed is scale: generative AI makes it much easier to produce large volumes of synthetic tables, text and images.

Types of synthetic data

How synthetic data is generated

There are 3 broad approaches, from simple to sophisticated:

1. Rules and libraries. Developers write rules (“order totals between $5 and $500, 3% refunds”) or use fake-data libraries to fill tables with plausible names, addresses and numbers. Cheap and fast, but it does not capture real-world patterns.
2. Statistical models. The generator learns distributions and relationships from real data, then samples new records from that model. The Census Bureau, for example, uses sequential regression methods to replace sensitive values.
3. Machine learning models. Deep generative models, such as GANs, variational autoencoders, diffusion models and large language models, learn complex patterns and produce records, text or images. These can produce highly realistic data, but they can also memorize and reproduce real examples if not controlled.

Some generators add differential privacy, a mathematical technique that limits how much any single real record can influence the output. NIST’s guidance on de-identifying government datasets (SP 800-188, finalized in 2023) lists synthetic data as one release option and cautions that traditional de-identification has limits compared with formal privacy methods like differential privacy.

When synthetic data helps

Software testing and development

This is the most common and lowest-risk use. Test environments need realistic data volumes and formats, but copying production data exposes customer information to more people and systems. Synthetic data lets you test edge cases, run load tests and demo features without real records. It fits well alongside automated testing.

Privacy-safer data sharing

Synthetic versions of sensitive datasets can be shared with vendors, researchers or internal teams who don’t need access to real records. This is especially relevant for health, finance and HR data. “Safer” is the key word, not “safe”: synthetic data still needs testing for re-identification risk.

AI and machine learning training

Synthetic data can fill gaps where real data is scarce, expensive or imbalanced. Examples include rare fraud patterns, unusual manufacturing defects, edge cases for self-driving systems and underrepresented groups in a dataset. It can also help bootstrap a model before enough real data exists. See how large language models are trained for how training data shapes models.

Simulation and planning

Simulated data lets teams test “what if” scenarios that haven’t happened yet, such as a supply disruption, a demand spike or a new store location, without waiting for real events.

Key takeaway: Synthetic data is most useful where real data is risky, scarce or slow to get, and least reliable when you need to understand rare, real-world behavior you haven’t already captured. Use it to supplement real data, not to replace it.

The risks of synthetic data

It can be unrealistic

A generator only knows the patterns it learned. If the real data was small, messy or missing important cases, the synthetic version will be too, only now it looks clean and complete. Models trained mostly on synthetic data can perform well in testing and poorly in the real world. Rare but important outliers, the exact cases fraud and safety systems care about, are often smoothed away.

It can carry over or amplify bias

If the source data underrepresents certain groups or reflects past unfair decisions, synthetic data built from it will usually reproduce those patterns. Some teams use synthetic data to rebalance datasets, but that requires deliberate design and testing. Our explainer on AI bias covers how this shows up.

It can still leak private information

Generative models can memorize parts of their training data. A synthetic dataset may include records that are near-copies of real people, especially those with unusual combinations of traits. Synthetic data is not automatically anonymous, and depending on how it was produced, privacy laws may still apply. Check obligations under US state privacy laws and, for health data, HIPAA. This is not legal advice; involve your privacy counsel.

Training AI on AI output can degrade models

A 2024 study in Nature by Shumailov and colleagues found that training models indiscriminately on content generated by earlier models causes “model collapse,” in which the rare parts of the original data distribution disappear over successive generations. The authors showed the effect in large language models and other model types, and argued that data from genuine human interactions will become more valuable as AI-generated content spreads. The lesson for businesses: keep real data in the mix and track which training data is synthetic.

Synthetic data vs anonymized data vs real data

A checklist for using synthetic data safely

Before you rely on a synthetic dataset, check these points:

• Purpose is clear. Testing, sharing and model training need different quality levels. Write down which one you need.
• Fidelity is measured. Compare distributions, correlations and summary statistics against the real data. For models, compare performance on a real holdout set.
• Edge cases are checked. Confirm rare categories and outliers you care about appear in realistic proportions.
• Privacy is tested. Look for near-duplicates of real records and run re-identification or membership tests. Consider differential privacy for sensitive data.
• Bias is reviewed. Check outcomes across groups before and after generation.
• It is labeled. Mark synthetic datasets clearly in your data catalog so nobody mistakes them for real figures in a report.
• Real data stays in the loop. Validate final models and decisions on real data.

Where to start

1. Start with test data. Replace copies of production data in development and QA environments with synthetic or masked data. It is the quickest win and reduces exposure.
2. Pick 1 contained AI use case. For example, augment rare fraud or defect examples, and measure whether the model improves on real holdout data.
3. Choose a tool that matches the job. Fake-data libraries work for testing. Statistical or ML-based generators, from open-source libraries or commercial platforms, suit analytics and AI. Pricing models vary, so compare on your data volume.
4. Bring in privacy and security early. Agree on how you will test for leakage before any synthetic dataset leaves your team. Our guide on whether it’s safe to put company data into AI covers related risks.
5. Document everything. Record the source data, generator, settings and validation results so you can explain and reproduce the dataset later.

For a broader readiness plan, see how to prepare company data for AI.

Conclusion

Synthetic data is a practical tool for testing software, sharing data more safely and filling gaps in AI training sets. Its weaknesses are just as real: it can be unrealistic, biased or leaky, and training models on AI output without real data can degrade them. Use it for a defined purpose, measure how closely it matches reality, test for privacy leaks and keep real data in the loop. For related terms, see our glossary.

Sources

• NIST SP 800-188: De-Identifying Government Datasets: Techniques and Governance — synthetic data as a release option, differential privacy and limits of traditional de-identification
• US Census Bureau: SIPP Synthetic Beta data product — a government synthetic data product linking survey and administrative records
• US Census Bureau: The Creation and Use of the SIPP Synthetic Beta — synthesis method using sequential regression
• Shumailov et al., “AI models collapse when trained on recursively generated data,” Nature 631 (2024) — model collapse from training on model-generated data