What is synthetic data in AI?

Explained

Synthetic data is data created by a model or simulation rather than collected from real events, built to keep the useful statistical properties of an original dataset. It is used to enlarge scarce datasets, balance rare cases, test systems without exposing personal data and simulate situations that are hard to observe. Its quality depends on the original data and on the model that generates it: it can miss outliers, inherit the biases of the source and, if poorly made, still leak information about real individuals. Governance is needed to check that synthetic data is fit for purpose and does not encode harmful bias.

How synthetic data is generated

• Rules and simulation: data produced from business rules, physical models or simulators, common in industry and robotics.
• Statistical models: methods that learn the distributions and correlations of a real table and sample new rows.
• Generative models: neural networks, including large language models, that produce new records, text, images or conversations resembling the originals.

In every case, a model is trained or configured on real data first, so the original data still matters.

What it is used for

• Training and testing models where real examples are few, such as fraud, rare failures or edge cases.
• Developing and testing software with realistic data, without copying production data into lower environments.
• Sharing data with partners or teams when the original contains personal or confidential information.
• Building evaluation sets for AI systems, for example synthetic questions to test a RAG assistant.

Limits and risks

Synthetic data is not automatically anonymous. If the generator memorises real records, information about individuals can leak, so privacy needs to be tested, not assumed.

It also reflects its source: biases in the original data carry over, and rare cases or outliers are often under-represented. Before using it, compare it with the real data on the properties that matter for the use case and keep track of how it was produced.

Frequently asked questions

Decision guides that use this term

• Synthetic data vs real data: what should you train, tune and test your AI on?

Further reading

• AI Fairness Monitoring: From Defensive Cost to Strategic Advantage
• Smart Data Curation Is the New Scale for Enterprise AI

Related terms

• Bias Systematic distortion in the data, design or use of an AI system that produces unfair, discriminatory or inaccurate results for some people or cases.
• Big Data Data sets so large, fast-moving or varied that traditional databases and tools cannot store or process them efficiently; a key input for AI.
• Context Engineering The practice of deciding what information a model sees at each step (instructions, retrieved documents, tool results, memory) and fitting it into the context window.
• Corpus A collection of documents or texts used to train a model or to serve as the knowledge base an AI system retrieves from, as in RAG.
• Data Lake A central repository that stores structured and unstructured data in its raw form, as a source for analytics, machine learning and AI.
• Data Pipeline An automated sequence of steps that collects data from its sources, cleans and transforms it, and delivers it to the models, applications or analytics that use it.