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Yes—but “synthetic” is not a regulatory exemption. Under EU law, the answer depends on how the records were generated, whether the data or resulting model can still relate to identifiable people, and whether the dataset is suitable for its intended AI use. This EU-focused explanation is current to 7 October 2026; other jurisdictions and sector-specific rules may differ.
Can synthetic data be used to train AI?
There is no blanket EU AI Act ban on synthetic training data, and the Act does not declare it compliant by default. For a high-risk AI system, synthetic records can be used only as part of a dataset and governance process that meet the requirements for the system’s intended purpose. Separately, data-protection law may apply to the source records, the act of generating synthetic records, or data that remain identifiable after generation.
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It helps to distinguish three stages: collecting and processing source data; generating synthetic data from those sources; and using the generated records to train, validate or test a model. Each stage raises different questions. The European Data Protection Board (EDPB) explains that generating synthetic data from personal records can itself involve processing personal data, even if the resulting dataset is not personal data. The French data-protection authority CNIL likewise says a training dataset containing personal data needs a legal basis under the GDPR.
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Is synthetic data GDPR compliant?
Not automatically. Whether the GDPR applies turns on whether personal data are processed, not on whether a dataset is labelled synthetic. A genuinely synthetic output may fall outside the GDPR if it does not refer to an identified or identifiable person. But the processing used to create it can still be subject to the Regulation, and a dataset that keeps real names associated with generated values can remain personal data—even if those values are inaccurate.
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Check the full data pipeline
- Source data: Identify whether the records used to generate the synthetic data contain personal data and establish a lawful basis for processing them. Also assess the purpose and other applicable obligations for that processing.
- Generation: Treat the generation step as potentially involving personal-data processing when it uses real personal records. Document what data go into the process and what safeguards apply.
- Generated dataset: Assess whether the output still relates to identifiable people, including through retained identifiers or associations. “Synthetic” alone does not establish anonymity.
- Model and later use: Consider whether personal data can be extracted from a model trained on personal data, and whether the dataset is appropriate for the model’s intended use.
These are related but distinct assessments: a dataset may not itself be personal data while its generation still involved processing personal data, and the anonymity of a trained model requires a separate assessment.
Does synthetic data count as personal data?
It can, depending on whether it refers to an identified or identifiable person. The EDPB cautions against treating either synthetic datasets or models trained on personal data as automatically anonymous. Its Opinion 28/2024 calls for a case-by-case assessment of whether it is very unlikely both that people whose data were used can be identified directly or indirectly and that their personal data can be extracted from the model through queries.
That assessment includes risks such as extraction and accidental disclosure. Removing obvious identifiers, pseudonymising records or generating new values does not by itself prove anonymity. Privacy-enhancing approaches such as differential privacy and validation may help mitigate risk, but the EDPB materials do not establish a universal legal threshold or safe harbour.
What does the EU AI Act require for high-risk AI data?
For high-risk AI systems using model-training techniques, the EU AI Act requires training, validation and testing datasets to be governed and managed appropriately for the intended purpose. The consolidated Regulation dated 27 July 2026 does not exempt synthetic data from those requirements. The question is whether the data and the process that produced them are suitable and adequately governed for the system’s context.
Governance and documentation
Article 10 calls for appropriate data-governance and management practices that address matters including:
- Design choices and data-collection processes, including the source and, for personal data, the original collection purpose.
- Preparation steps such as annotation, labelling, cleaning, updating, enrichment and aggregation.
- Assumptions about what the data measure and represent, as well as their availability, quantity and suitability.
- Biases likely to affect health, safety or fundamental rights, or to result in prohibited discrimination, together with measures to detect, prevent and mitigate them.
- Data gaps and shortcomings.
The datasets must also be relevant and sufficiently representative, and, to the best extent possible, free of errors and complete for their intended purpose, with appropriate statistical properties. They should account for the relevant geographical, contextual, behavioural or functional setting. Synthetic records therefore need evidence of fitness for the target population and use; the label does not establish either.
Recital 67 says these quality requirements should not affect the use of privacy-preserving techniques. The Act also notes that third-party compliance services can support data-governance verification, dataset integrity and training, validation and testing practices where compliance is ensured. Neither point removes the need to establish that the data are fit for purpose.
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Article 10(5) is a narrow provision for providers of high-risk AI systems processing special categories of personal data to detect and correct bias. One condition is that the aim cannot be effectively fulfilled by processing other data, “including synthetic or anonymised data.” This means the provider must consider those alternatives before relying on special-category data for that purpose.
The provision also requires safeguards: technical limits on reuse, state-of-the-art security and privacy-preserving measures including pseudonymisation, suitable safeguards and strict access controls, and restrictions on transmission or access by other parties. It is not a general endorsement of synthetic data, a certification of anonymity or an exemption from dataset-quality requirements.
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How do real, synthetic and anonymised data differ in practice?
These labels describe different things and are not interchangeable legal categories. Synthetic data describes how records are produced; anonymisation concerns whether people can still be identified. The practical assessment should consider both privacy risk and whether the data remain useful and representative for the intended system.
| Data type | Processing and privacy question | Fitness and governance question |
|---|---|---|
| Real data | If records identify or can identify people, their collection and use can involve personal-data processing. | Assess relevance, representativeness, errors, completeness, bias and context for the intended use. |
| Synthetic data | Generation from personal records can itself involve processing; assess whether outputs or the trained model still relate to identifiable people or allow extraction. | Assess fidelity to the target population, representativeness, bias and suitability for the intended use. |
| Anonymised data | Assess whether people can still be identified directly or indirectly; the label alone does not establish anonymity. | Assess the same intended-purpose and quality needs that apply to the system’s datasets. |
There is no single privacy-versus-utility score that settles these questions. EDPB training materials describe potential uses such as privacy-sensitive research, data augmentation and simulation of rare or high-risk scenarios, alongside trade-offs involving fidelity, resemblance to originals, re-identification risk and computational overhead. A dataset that reduces exposure to original records may still be biased or a poor fit for the real-world population the system will encounter.
Are AI Act transparency duties the same as data-protection compliance?
No. General-purpose AI (GPAI) providers have separate obligations under Article 53 to maintain a copyright policy and publish a summary of training content, subject to the Regulation’s scope and exceptions. The European Commission says GPAI obligations began applying on 2 August 2025. The AI Act generally became applicable on 2 August 2026, with exceptions.
Those transparency duties do not establish that source data were lawfully processed, that a generated dataset or model is anonymous, or that a dataset is suitable for a high-risk system. Copyright policy and training-content summaries should be considered alongside—not as substitutes for—data-protection and dataset-governance assessments.
How should a team assess a synthetic dataset?
For an operational decision, document the answers to these questions against the specific data, purpose and AI system:
- What source records and collection purposes fed the generation process, and what legal basis supports any personal-data processing?
- Does generation or later use retain identifiers or associations, or create a risk that people can be identified or personal data extracted from the model?
- How closely do the synthetic records reflect the target population and the system’s geographical, contextual, behavioural or functional setting?
- What evidence supports the data’s relevance, representativeness, completeness and suitability, and how are errors, gaps and bias evaluated and addressed?
- What safeguards govern access, reuse and onward sharing, especially if special-category data are considered for the limited bias-correction purpose in Article 10(5)?
The answers depend on the source data, generation method, system classification and intended use. The EU rules described here do not settle requirements in the United States or every other jurisdiction, and other laws may apply depending on purpose, geography and rights in source material.
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