Quick answer: synthetic data is information generated by an algorithm, a simulation, or a generative model rather than recorded from real people or events, built to match the statistical patterns of a real dataset without containing any actual record. Teams use it to train and test AI systems when real data is scarce, sensitive, or missing the rare cases a model needs to see. A self-driving car trained partly on simulated crashes it has never actually had is a common example.
This entry covers what counts as synthetic data, how it gets generated, where it already shows up in production AI, how it differs from data augmentation, and the risks that come with relying on it.
What Is Synthetic Data?
Synthetic data is artificial from the start. Nobody’s medical record, face, or transaction sits behind it; a model or a simulator produced it to look and behave statistically like data that could be real.
That is different from data that has merely been stripped of names and addresses. Anonymized data still describes real people, with identifying details removed. Synthetic data describes nobody.
Teams reach for it for three overlapping reasons. Privacy comes first: no real patient or customer record is exposed if the synthetic set leaks. Scarcity is second: there are not enough real examples of a rare event to train on reliably.
Control is third. A simulator can generate exactly the edge case a model keeps missing, on demand, rather than waiting for one to happen in the real world.
The term is not new. Statisticians were generating artificial datasets to test models decades before the current wave of AI, and the same basic idea, standing in for a real record with a synthetic one, still holds. What changed is that generative models now produce synthetic images, audio, and text realistic enough to train other AI systems on, not just to run a statistical test.
How Is Synthetic Data Generated?
The method depends on what kind of data is needed. Generative adversarial networks and diffusion models, the same family of techniques behind generative AI image tools, produce synthetic images and video by learning the visual patterns in a real dataset and sampling new examples from that learned distribution.
Large language models generate synthetic text: question-and-answer pairs, dialogue, or step-by-step reasoning examples used to train smaller models. Microsoft’s Phi family of small language models, including its vision-capable versions, was built largely on synthetic, textbook-style data created for that purpose rather than scraped from the open web.
Simulation engines handle a third category: physics-based environments that generate synthetic sensor data. Waymo builds and drives far more simulated miles than real ones, placing realistic renderings of cars and pedestrians into scenes assembled from its own computer vision sensor logs, so the system can rehearse rare situations a real test fleet would rarely encounter.
Healthcare uses a similar approach for structured records. Synthea, an open-source tool originally built at MITRE, generates entirely fictional patients with realistic longitudinal medical histories, used for research and software testing where real patient data would raise privacy problems.
Structured business data gets a different treatment again. Statistical models learn the relationships between columns in a real spreadsheet, such as income and loan default, and generate new rows that preserve those relationships without copying any real customer’s row.
Where Is Synthetic Data Used?
Autonomous vehicles are the clearest case. A self-driving system needs to see thousands of near-misses and rare weather conditions before it ever meets one on the road, and simulated driving covers that gap far more cheaply than a real test fleet ever could.
Large model training is close behind. Several current language and vision models mix real web data with synthetic examples generated specifically to teach reasoning, code, or math that the open web does not cover well enough on its own.
Healthcare and finance both use synthetic records to test software and share realistic data across teams without exposing a real patient or customer. Fraud detection systems train on synthetic transaction patterns to cover fraud types too rare to appear often enough in a bank’s own history.
Robotics is a newer addition. Warehouse and manufacturing robots increasingly train on simulated environments first, learning to grasp and navigate in a virtual replica of a facility before a real robot ever touches the floor, which cuts both cost and the risk of damaging real equipment during early training.
Synthetic Data vs Data Augmentation: What’s the Difference?
Data augmentation starts from a real example and transforms it: flipping, cropping, rotating, or adding noise to a real photo to create more training variety. The original real record is still in there, just altered.
Synthetic data has no original behind it. A generative model or simulator produces a new example from scratch, based on patterns learned from real data but not tied to any single real record.
The distinction matters most for privacy. Augmented data can sometimes be traced back to the real example it started from. Well-built synthetic data cannot, because there is no real record to trace.
What Are the Risks of Synthetic Data?
The biggest one is quiet and compounding. When a model trains on data generated by an earlier model, and that cycle repeats across generations, researchers have documented a failure called model collapse: the model gradually loses the rare, unusual examples found in the real world and drifts toward a blurrier, less accurate average.
Domain gap is the more everyday version of the same problem. Synthetic scenes that look realistic to a person can still miss subtle statistical patterns that real sensors produce, and a model trained mostly on that gap can perform worse the moment it meets real footage.
Bias can get amplified rather than removed. A generator trained on a skewed real dataset tends to reproduce that skew in the synthetic data it produces, sometimes more strongly than the original.
There is also a plain quality problem: synthetic data is only as trustworthy as the model or simulator that made it. A generator that has never seen a genuinely novel real-world pattern cannot invent one on its own; it can only recombine what it already learned, which is why the most reliable pipelines treat synthetic data as a supplement to real evidence rather than a replacement for it.
Where Is Synthetic Data Headed?
Synthetic data has moved from a gap-filler to a product category of its own. Nvidia now ships world foundation models under the Cosmos name specifically as synthetic data engines for robots and autonomous vehicles, and robotics companies use them to generate the sensor footage their machines train on rather than recording all of it.
Language model training went the same direction. Teaching reasoning, code, and math with purpose-built synthetic examples is now ordinary practice rather than an experiment, which is a large part of why small models keep getting better faster than the supply of good web text grows.
None of this makes synthetic data a shortcut around real evidence. Used alongside real data, with genuine examples still anchoring the mix, it remains one of the more practical tools for covering a gap that real-world collection alone closes too slowly.