What Is Synthetic Data?

Synthetic data is artificially generated data that mimics the statistical properties, structure, and relationships of real-world data — without containing actual records from real individuals or systems. It is produced algorithmically, not collected from real-world events or people, which means it carries no inherent privacy risk related to the individuals whose data informed its generation.

Synthetic data has moved from a research curiosity to a mainstream enterprise capability, driven by two converging forces: tightening data privacy regulations that restrict access to personal data, and the insatiable appetite of AI and machine learning models for large, diverse, labeled training datasets. When real data is unavailable, too sensitive, or insufficient in quantity or diversity, synthetic data fills the gap.

Synthetic Data Defined

Synthetic data exists on a spectrum of how closely it mirrors real data:

• Fully synthetic data — Generated entirely by algorithms with no direct correspondence to any real record. The statistical properties of the real dataset inform the generation process, but no synthetic record can be traced to a real individual.
• Partially synthetic data — Real records with sensitive fields replaced by synthetically generated values. Common in scenarios where non-sensitive attributes (product IDs, transaction types) can be retained while personal identifiers are synthesized.
• Augmented data — Synthetic additions to real datasets to address underrepresentation. If a healthcare dataset has 95% non-minority patients, synthetic minority patient records can be generated to create a more balanced training set.

The distinction matters for privacy: fully synthetic data is generally considered to fall outside GDPR scope (there are no real individuals whose data is processed). Partially synthetic data retains some real records and requires careful analysis to confirm that the synthetic replacement is sufficient for de-identification.

How Synthetic Data Is Generated

Multiple generation approaches have emerged, each with different use cases and fidelity trade-offs:

Statistical and Rule-Based Generation

The simplest approach: learn the statistical distributions of each column from real data (mean, variance, correlation with other columns) and sample from those distributions. Fast and interpretable, but struggles to capture complex multivariate relationships and may miss rare but important patterns in the real data.

Generative Adversarial Networks (GANs)

A generator model learns to produce synthetic records while a discriminator model attempts to distinguish synthetic from real. The two models train adversarially until the generator produces data that the discriminator can't reliably identify as synthetic. GANs can capture complex, high-dimensional relationships but are sensitive to training instability and can fail to represent rare events.

Variational Autoencoders (VAEs)

Encode real data into a compressed latent representation, then decode samples from that latent space as synthetic records. More stable than GANs and better at capturing the full data distribution, including tail events.

Large Language Models for Tabular Data

Frontier LLMs and purpose-built models (like MOSTLY AI, Gretel, or Tonic) can generate synthetic tabular data by treating rows as sequences and sampling from the learned conditional distributions. This approach has shown strong fidelity for complex relational data.

Agent-Based Simulation

For synthetic data representing process outcomes (customer journeys, fraud patterns, clinical trial responses), agent-based simulation models the real-world process that generates the data. High interpretability and domain alignment, but requires significant domain modeling investment.

Use Cases

Synthetic data addresses scenarios where real data is unavailable, inaccessible, or insufficient:

• Software and system testing — Development and QA teams need realistic test data that doesn't expose production personal data in test environments. Synthetic data provides high-fidelity test data without privacy risk. This is the most mature and widely adopted use case.
• Analytics development — Data engineers building pipelines, analysts prototyping queries, and data scientists exploring new approaches can work with synthetic data before gaining access to restricted production datasets. Faster iteration, no privacy exposure.
• ML training data augmentation — Addressing class imbalance (rare fraud patterns, rare disease cases) by generating synthetic minority-class examples. Also used when labeled real data is insufficient for training a robust model.
• Regulatory compliance — In highly regulated industries (finance, healthcare), sharing real data with third parties (vendors, researchers, regulators in other jurisdictions) may be restricted. Synthetic data can be shared freely.
• Product demos and development — Building and demonstrating SaaS products without accessing real customer data. Particularly relevant for data-intensive products where demo environments need realistic but not real data.

Synthetic Data for AI and ML

The most significant growth in synthetic data use comes from AI and machine learning. Three AI use cases drive adoption:

Training Data Generation

Large language models and other foundation models require vast amounts of training data. Synthetic data — generated by existing models, rule-based systems, or simulation — supplements real training corpora, addresses gaps in coverage, and allows controlled diversity. This has become a critical capability for organizations fine-tuning domain-specific models where real-world examples are scarce.

Evaluation Dataset Generation

Testing LLM and RAG systems requires representative evaluation datasets. For enterprise AI, these need to include realistic examples of the queries users will ask, with known expected answers. Synthetic evaluation sets can be generated at scale for specific domains without the manual labeling effort that real evaluation datasets require.

AI Safety and Red-Teaming

Testing AI systems for harmful behaviors requires adversarial examples that might not occur naturally in production data. Synthetic adversarial examples — generated to probe specific failure modes — are used in safety evaluation without needing to expose real user data to test pipelines.

Limitations and Risks

Synthetic data is not a universal solution. Critical limitations:

• The training data quality ceiling — Synthetic data can only be as good as the real data it was generated from. If the real data has biases, the synthetic data inherits them. If the real data is sparse for certain subgroups, the synthetic data will fail to represent them adequately.
• Distribution shift — Synthetic data is a model of the past distribution of real data. Real-world distribution changes (new customer segments, new fraud patterns, new product types) won't be reflected in synthetic data until it's regenerated from updated real data.
• Privacy is not guaranteed — Improperly generated synthetic data can memorize and reproduce real records, particularly for rare individuals whose patterns in the training data are distinctive. Privacy auditing — measuring re-identification risk — is an essential step before declaring synthetic data "safe."
• Rare event underrepresentation — The rarer an event in real data, the harder it is to generate faithful synthetic examples. For fraud detection, rare disease prediction, and other rare-event use cases, synthetic augmentation needs careful validation.

Governance and Quality

Synthetic data requires governance even though it's not real data:

• Source data governance — The real data used to train the synthetic generator must be well-governed: its quality, provenance, and representativeness determine synthetic data quality. Garbage in, garbage out applies with full force.
• Generation model versioning — As real data evolves, synthetic generators must be retrained and the synthetic datasets regenerated. The version of the generator used to produce a synthetic dataset should be tracked, like any other data lineage.
• Quality validation — Every synthetic dataset should be validated for statistical fidelity (does it match the real distribution?), ML utility (does a model trained on it perform well on real data?), and privacy (is re-identification risk within acceptable bounds?). These validations should be documented and repeatable.
• Catalog and classification — Synthetic datasets should be registered in the data catalog with metadata indicating they are synthetic, the source real dataset they were generated from, the generation method, and the validation results. This prevents teams from accidentally treating synthetic data as real or vice versa.

Conclusion

Synthetic data is a powerful capability for organizations navigating the tension between AI's need for data and privacy regulations' constraints on personal data use. Used well — with proper generation methods, rigorous validation, and governed as a first-class data asset — it unlocks AI development, testing, and analytics workflows that would otherwise be blocked by privacy restrictions. Used carelessly — without validation, without source data governance, or without privacy auditing — it creates a false sense of privacy protection while potentially leaking real information. The governance discipline applied to synthetic data should be commensurate with the sensitivity of the real data it was generated from.