Category: Data Governance & Quality

Synthetic Data

Also known as: Artificial Data, Synthetic Data Generation (SDG)

Simply put

Synthetic data is information created artificially by computer algorithms rather than collected from real-world events or measurements. It is typically designed to imitate the patterns and statistical properties of actual data so it can be used in place of, or alongside, real data. A common use is to help train AI models across domains such as robotics and autonomous vehicles.

Formal definition

Synthetic data is artificially generated data produced by algorithms—including statistical methods and generative AI techniques—rather than observed from real-world measurements or events. As commonly described, it is intended to mimic real-world data and can approximate the statistical properties of an underlying source dataset, making it applicable to tasks such as accelerating AI model training. The evidence provided describes synthetic data at a general level and does not specify the fidelity, privacy, or validation criteria that would typically be assessed in a model risk or governance context; those considerations are out of scope for this definition.

Why it matters

Synthetic data has gained prominence as a way to supply information for AI development when real-world data is scarce, costly to collect, or otherwise difficult to obtain. Because it is generated by algorithms rather than observed from real-world events, it can be produced at scale and used to accelerate AI model training across domains such as robotics and autonomous vehicles, as commonly described by vendors in this space. This makes it operationally attractive to teams building and testing models.

From a governance and model risk perspective, the fact that synthetic data is designed to mimic or approximate the statistical properties of real data raises questions that professionals typically want answered before relying on it: how closely the synthetic data reflects the underlying source, whether it introduces or masks bias, and how it should be validated for a given use. The evidence available here describes synthetic data only at a general level and does not establish fidelity, privacy, or validation criteria, so those assurances should not be assumed simply because data is labeled synthetic.

Because treatment of synthetic data varies by sector and by framework, its acceptability as a substitute for real data is context-dependent. Organizations should be cautious about treating synthetic data as inherently lower-risk; it manages certain data-availability challenges without automatically resolving concerns about representativeness, data quality, or model performance, all of which typically require independent assessment.

Who it's relevant to

Data Scientists and Model Developers

Practitioners who build and train AI models encounter synthetic data as a means to accelerate training when real-world data is limited. They typically need to consider how well the generated data reflects the intended real-world distribution, though the general definition here does not prescribe how to make that assessment.

Model Risk Managers

Those responsible for identifying, measuring, and controlling risks arising from model use may treat the use of synthetic data as a factor affecting data quality and representativeness. Because the evidence does not establish fidelity or validation criteria, such criteria would typically be defined and evaluated independently rather than assumed.

AI Governance and Policy Specialists

Professionals designing organizational policies and oversight for AI systems may need to set expectations for when and how synthetic data is acceptable, recognizing that its treatment can be context- and sector-dependent and that labeling data as synthetic does not by itself resolve governance questions.

Auditors and Validators

Reviewers assessing models and their inputs may need to trace whether training data was synthetic, how it was generated, and how it was validated. The general description provided does not supply validation standards, so auditors would typically look to an organization's own controls and any applicable requirements.

Inside Synthetic Data

Artificially Generated Records

Data points produced by algorithms or models rather than collected from real-world observation or events, intended to resemble the statistical properties of a source or target distribution.

Generation Method

The technique used to produce the data, which in common practice may range from simple rule-based or statistical sampling to generative models. The choice of method affects how closely the synthetic data reflects the intended distribution.

Source or Reference Data

The real dataset (where one exists) whose distribution, structure, or relationships the synthetic data is designed to approximate. Some synthetic data is derived from a reference set, while other synthetic data may be generated from assumptions or specifications without a direct source.

Fidelity

The degree to which synthetic data reproduces the statistical characteristics and relationships of the intended distribution. Higher fidelity does not by itself guarantee that the data is fit for a given purpose.

Privacy or Disclosure Properties

Characteristics relating to whether the synthetic data can reveal information about individuals in an underlying source dataset. Synthetic generation is often intended to reduce disclosure risk, but does not automatically eliminate it, so this is a component to be assessed rather than assumed.

Intended Use Case

The purpose for which the synthetic data is created, such as model development, testing, augmentation of limited data, or scenario analysis. The suitability of synthetic data is typically evaluated relative to a specific intended use.

Common questions

Answers to the questions practitioners most commonly ask about Synthetic Data.

Does using synthetic data automatically make a dataset privacy-safe or exempt from data protection obligations?

No. This is a common misconception. Synthetic data is generated to resemble the statistical properties of real data, but generation from real source data does not by itself guarantee that individuals cannot be re-identified or that sensitive attributes cannot be inferred. Depending on the generation method and the risk of information leakage, synthetic data may still carry privacy risk, and applicability of data protection obligations typically depends on the specific facts, jurisdiction, and whether the output is genuinely non-personal. Treat privacy safety as something to be assessed and evidenced, not assumed.

Is synthetic data a straightforward replacement for real data in model development and validation?

Not as a general rule. Synthetic data can supplement real data, but it inherits the limitations, biases, and assumptions of the process or model used to generate it, and it may fail to capture rare events, edge cases, or relationships present in real-world data. Relying on it as a full substitute can obscure model risk rather than reduce it. In many contexts practitioners treat synthetic data as one input among several, with its fitness for a given purpose evaluated case by case.

How should the fidelity of a synthetic dataset be assessed before use?

Fidelity is commonly assessed by comparing the synthetic data against the real source data across relevant dimensions, such as marginal distributions, correlations, and behavior in edge or tail regions, as well as by testing whether models trained or tested on the synthetic data behave consistently with those using real data. The appropriate metrics depend on the intended use, and a dataset that is adequate for one purpose may be inadequate for another. Fidelity assessment and privacy assessment are distinct exercises and are often in tension, so both should be documented.

What should be documented when synthetic data is used in a modeling workflow?

Documentation typically covers the generation method, the source data and its limitations, the intended purpose and scope of use, the fidelity and any privacy or leakage assessments performed, and the assumptions and known gaps of the synthetic dataset. Clear documentation supports review by second-line and independent validation functions and helps downstream users understand where the data is and is not fit for purpose. What is required in practice can vary by organization, sector, and applicable framework.

How does the use of synthetic data interact with model validation?

Because synthetic data reflects the assumptions of its generation process, validation activities should distinguish between conclusions supported by real data and those resting on synthetic data. Where synthetic data is used in testing, reviewers commonly consider whether it adequately represents the conditions the model will face in deployment, including rare or adverse scenarios. Using synthetic data does not remove the need for validation against real-world performance where feasible, and any reliance on synthetic inputs is typically noted as a limitation.

Who within an organization is typically involved in governing the use of synthetic data?

Responsibilities are often distributed across lines of defense: those who build or apply the synthetic data as part of model development, functions responsible for oversight and challenge of its appropriateness, and independent review or audit functions. The specific allocation depends on an organization's governance structure and the risk associated with the use case. Governance measures aim to manage and reduce risk arising from synthetic data rather than to eliminate it.

Common misconceptions

Synthetic data is automatically anonymous and therefore free of privacy or disclosure risk.

Reducing disclosure risk is often a goal of synthetic data generation, but it is not guaranteed. Depending on the generation method and the underlying source data, synthetic records can retain information that permits inference about real individuals, so privacy properties typically require explicit assessment rather than assumption.

Higher statistical fidelity to source data always makes synthetic data better.

Fidelity to the intended distribution and fitness for a specific use are distinct considerations. Very high fidelity can, in some cases, increase disclosure risk or simply reproduce limitations and biases present in the source, so fidelity should be evaluated against the intended purpose rather than treated as an unqualified good.

Synthetic data removes the need for validation and governance of models trained or tested on it.

Using synthetic data does not by itself reduce or eliminate model risk. Models developed or evaluated using synthetic data are still subject to the same validation, monitoring, and control expectations that apply to models more generally, and the appropriateness of the synthetic data itself may become an additional item for scrutiny.

Best practices

Document the generation method, any source or reference data used, and the intended use case so that reviewers can assess whether the synthetic data is fit for its purpose.

Evaluate fidelity relative to the specific intended use rather than treating maximal similarity to source data as inherently desirable.

Assess privacy and disclosure properties explicitly rather than assuming that synthetic generation makes the data anonymous.

Consider whether the synthetic data may reproduce biases or limitations present in the underlying source or in the assumptions used to generate it.

Treat models developed, augmented, or tested with synthetic data as subject to standard validation, monitoring, and control practices, and include the suitability of the synthetic data within that review.

Use qualified, use-specific claims about synthetic data quality, and record known limitations so downstream users understand what the data can and cannot support.