What Is Synthetic Data?

Privacy-preserving training data

Generating artificial records that mirror the statistical structure of sensitive datasets (health records, financial transactions, personal identifiers) so models can be trained or shared without exposing real individuals, often combined with differential-privacy techniques applied during generation.

Data augmentation

Expanding thin or imbalanced datasets, for example rare disease cases or minority-class fraud examples, with generated examples so models see enough variety to learn robust, generalizable patterns.

Software and model testing

Producing realistic test datasets for QA, stress-testing, and edge-case simulation without needing production data or waiting for real incidents to occur naturally.

Bias detection and rebalancing

Creating supplementary examples for underrepresented groups or scenarios to help surface and correct skewed training distributions before deployment.

Cross-organization data sharing

Allowing organizations to share representative datasets with researchers, vendors, or regulators when the original data cannot leave the organization for privacy or competitive reasons.

Model collapse

Nature-published research (Shumailov et al., 2024) found that generative models trained recursively on their own synthetic output progressively lose the tails of the original data distribution, degrading diversity and quality over successive generations, a risk NIST's Generative AI Profile (AI 600-1) now flags explicitly.

Bias and quality amplification

Synthetic data generated from a biased model or a skewed seed dataset can reproduce, and in some cases exaggerate, that same skew. NIST groups this under "harmful bias and homogenization" in its Generative AI Profile risk taxonomy.

Residual privacy leakage

Because generative models such as GANs can memorize training examples, synthetic outputs can still leak identifiable patterns from real records unless privacy-preserving techniques (e.g., differentially private GANs) are deliberately built into generation.

Fidelity and traceability gaps

Quality frameworks for synthetic data emphasize verisimilitude (how closely it matches real-world structure), consistency, and traceability (being able to track how and from what it was generated), weak provenance makes downstream model behavior hard to audit.

Undisclosed synthetic content

NIST AI 100-4 addresses the broader risk that AI-generated ("synthetic") content, images, audio, text, can be passed off as authentic without disclosure, undermining transparency and public trust.

What is synthetic data in AI, in simple terms?

It's artificial data created by an algorithm, model, or simulation to imitate the statistical patterns of real data, without containing any actual real-world records. NIST describes synthetic data generation as using seed data "to create artificial data that have some of the statistical characteristics of the seed data."

Is synthetic data as good as real-world data for training AI?

It's very useful for augmentation, testing, and privacy protection, but quality depends heavily on the generation method and seed data. Research published in Nature in 2024 showed that repeatedly training generative models on their own synthetic output, instead of mixing in fresh real-world data, can cause measurable degradation in output diversity and quality over successive generations.

What is model collapse and why does it matter for AI governance?

Model collapse is the progressive degradation of a generative model's outputs when it is trained recursively on data produced by earlier AI models rather than real-world data. Shumailov et al.'s 2024 Nature paper showed this causes models to lose rare, minority patterns from the true data distribution, converging toward narrower, lower-quality output, a risk NIST's Generative AI Profile (AI 600-1) now explicitly tracks under bias and homogenization.

Does synthetic data eliminate privacy risk?

Not automatically. Generative models such as GANs can memorize and reproduce fragments of their training data, so poorly generated synthetic data can still leak identifiable information. Meaningful privacy protection generally requires deliberately building in techniques like differential privacy during generation, not just labeling the output "synthetic."

How is synthetic data different from anonymized or de-identified data?

Anonymized or de-identified data starts as real records with identifiers removed or masked, so its re-identification risk depends on how thoroughly it was stripped. Synthetic data is generated from scratch by a model or algorithm and is not itself a copy of real records, though it must still be checked for whether it inadvertently reproduces identifiable patterns from its training source.

How much AI training data today is synthetic?

Precise, independently verified figures are scarce, but a widely cited 2021 Gartner forecast projected that synthetic sources would supply 60% of data used in AI and analytics development by 2024, up from about 1% in 2021. That trend is a key reason governance frameworks now treat synthetic data as a first-class risk category rather than a footnote.