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Yes—but synthetic training data is not a way to sidestep regulation. In the EU, the rules can apply to personal data used to generate synthetic records, to outputs or models that still relate to identifiable people, and to the quality and governance of data used by certain AI systems. The EU AI Act expressly mentions synthetic data in a limited bias-correction provision; it does not give synthetic datasets a general exemption or automatic approval. This article reflects the EU framework as of 7 October 2026. Other jurisdictions and sector-specific rules may differ.
Can synthetic data be used to train AI?
Yes. The EU AI Act does not ban synthetic training data. But calling a dataset synthetic does not, by itself, establish that its creation was lawful, that its records are anonymous, or that it is suitable for the AI system being trained.
It helps to separate three stages: processing source data, generating synthetic records, and using those records to train, validate, or test a model. Each can raise a different question. The GDPR may apply to processing personal data during the first two stages; the status of the resulting dataset depends on whether it relates to identifiable people; and the AI Act can impose data-governance requirements on high-risk AI systems.
Is synthetic data GDPR compliant?
Check the source data and the generation process
Generating synthetic records from personal records can itself be personal-data processing. The European Data Protection Board (EDPB) makes this distinction in its AI training materials: the generation step may involve personal data even when the output dataset, if it genuinely no longer relates to an identified or identifiable person, may not be personal data.
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The French data-protection authority CNIL says that creating and using a training dataset containing personal data requires a legal basis under the GDPR. That question does not disappear because the intended output is synthetic. Organisations also need to consider the purpose and other conditions that apply to their processing.
Check whether the output still relates to a person
“Synthetic” and “anonymous” are not interchangeable labels. A dataset can retain personal data—for example, if it keeps real names and associates them with generated values, even inaccurate ones. Whether a dataset falls outside the GDPR turns on whether it refers to an identified or identifiable person, not on whether its values were generated rather than copied.
The same caution applies to trained models. In Opinion 28/2024, the EDPB said that AI models trained on personal data cannot all be considered anonymous. Its case-by-case assessment asks whether it is very unlikely that people whose data were used can be identified directly or indirectly, and whether personal data can be extracted from the model through queries. Removing obvious identifiers, using pseudonymisation, or generating new records does not automatically settle those questions.
How do real, synthetic, and anonymised data differ?
These labels describe different things. Real data may directly represent people. Synthetic data is generated, often from source data, and may still disclose or remain associated with information about people. Anonymised data is data that no longer relates to an identified or identifiable person. The label alone does not demonstrate that the legal test for anonymity is met.
| Data type | Source-data processing | Main identity or disclosure question | Fitness for training |
|---|---|---|---|
| Real data | If it contains personal data, collecting and using it are subject to applicable GDPR requirements. | People may be identifiable directly or indirectly in the records. | Assess relevance, representativeness, errors, completeness, bias, and fit for the intended system. |
| Synthetic data | Generating it from personal records can itself be personal-data processing. | Check whether records still relate to identifiable people, can be associated with them, or expose personal data through a trained model. | Check fidelity to the target population and purpose as well as privacy risk; generated records are not automatically representative or accurate. |
| Anonymised data | The process used to anonymise personal data may involve processing personal data. | Determine whether people can still be identified or the data can still be linked to them. The label “anonymised” is not proof. | Assess whether the transformed data remain suitable for the specific training, validation, or testing purpose. |
The EDPB describes synthetic-data applications such as privacy-sensitive research, data augmentation, and simulation of rare or high-risk scenarios. It also describes trade-offs: synthetic records may resemble originals enough to create re-identification risk, and generation can add computational overhead. The practical comparison is therefore not simply privacy versus usefulness. It includes privacy risk, statistical fidelity, representativeness, and suitability for the actual use.
What does the EU AI Act require for high-risk AI data?
Governance must match the system’s intended purpose
For high-risk AI systems that use model-training techniques, Article 10 of the AI Act requires training, validation, and testing datasets to be subject to appropriate data-governance and management practices. The consolidated Regulation (EU) 2024/1689 dated 27 July 2026 sets out matters those practices cover, including:
- Design choices and the origin of data, including the original collection purpose where personal data are involved.
- Preparation processes such as annotation, labelling, cleaning, updating, enrichment, and aggregation.
- Assumptions about what the data measure and represent, along with their availability, quantity, and suitability.
- Whether the data may contain bias that could affect health, safety, or fundamental rights, or lead to prohibited discrimination.
- Measures to detect, prevent, and mitigate relevant bias, and to identify data gaps or shortcomings.
Representativeness and quality are use-specific
The datasets must be relevant and sufficiently representative, and, to the best extent possible, free of errors and complete for their intended purpose. They must have appropriate statistical properties and account, as required by that purpose, for the specific geographical, contextual, behavioural, or functional setting in which the high-risk system will be used.
These are fitness-and-governance requirements, not a rule that synthetic data are forbidden or automatically acceptable. A synthetic dataset that obscures a problem in the target population, fails to represent the intended setting, or contains errors may be unsuitable even if it reduces exposure to identifiable records. The AI Act’s Recital 67 also says its quality requirement should not affect the use of privacy-preserving techniques.
What does Article 10(5) say about synthetic data and bias correction?
Article 10(5) addresses a narrow case: a high-risk AI provider processing special categories of personal data to detect and correct bias. It requires that the aim cannot be effectively fulfilled by processing other data, including synthetic or anonymised data. If special-category personal data are used, the provision also requires safeguards, including technical limits on reuse, state-of-the-art security and privacy-preserving measures such as pseudonymisation, suitable safeguards and strict access controls, and restrictions on transmission or access by other parties.
This provision makes synthetic or anonymised data a possible alternative to consider in a defined bias-correction context. It does not certify a synthetic dataset, establish that all synthetic data are anonymous, or remove the need to assess whether data and methods are suitable for the task.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Are GPAI transparency duties the same as data-protection compliance?
No. The AI Act’s general-purpose AI (GPAI) provisions include duties for providers to maintain a copyright policy and publish a summary of training content under Article 53, subject to the Regulation’s scope and exceptions. The European Commission reports that GPAI obligations began applying on 2 August 2025; the AI Act generally became applicable on 2 August 2026, with exceptions.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThose transparency duties answer different questions from the GDPR and high-risk data-governance rules. A public summary of training content does not, by itself, establish a lawful basis for processing source data, prove that a dataset or model is anonymous, or show that a high-risk system’s data are fit for purpose.
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How should an organisation assess a synthetic-data pipeline?
- Map the pipeline. Identify the source data, the generation process, and each later use of the output for training, validation, or testing.
- Assess personal-data processing. Where personal data are collected or used to create the synthetic records, determine the applicable GDPR basis and requirements for that processing.
- Assess residual identification and extraction risk. Examine whether the records still relate to identifiable people and, for a trained model, whether people can be identified or personal data extracted through queries. Do not treat pseudonymisation or a synthetic label as proof of anonymity.
- Test fitness for the intended context. Evaluate representativeness, statistical properties, errors, completeness, bias, and the relevant geographical, contextual, behavioural, or functional setting.
- Keep governance evidence. Document data origins and preparation, design assumptions, suitability, identified gaps, bias evaluation, and mitigation in line with the requirements that apply to the system.
- Check obligations beyond the dataset. Establish whether the system is high-risk and whether GPAI provider obligations or other rules concerning purpose, geography, or rights in source material also apply.
When does synthetic training data make sense?
Synthetic data can be useful where it supports privacy-sensitive work, augmentation, or simulation of rare scenarios. It is not a universal substitute for real or anonymised data: generated records may lose important characteristics of the target population, preserve patterns that create disclosure risk, or fail to reflect the setting in which a system will operate. Differential privacy and validation may help manage risk, but the EDPB materials do not establish either as a universal legal safe harbour or set a universal compliance threshold.
The EU framework current to 7 October 2026 therefore permits synthetic training data without treating it as a regulatory loophole. Compliance depends on the whole pipeline: how the source data are processed, whether the outputs or model remain linked to people, and whether the data and governance are adequate for the system’s purpose.
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