One of the sharpest tensions in AI development is the need for large, diverse training datasets versus increasing regulatory constraints on using real personal data. GDPR, Australia’s Privacy Act amendments, and similar frameworks worldwide make it risky and expensive to train models on actual customer data, health records, financial transactions, or any information connected to identifiable individuals.

Synthetic data—artificially generated data that mimics the statistical properties of real data without containing actual personal information—offers an appealing solution. Generate fake customer records, medical histories, or transaction logs that look realistic but don’t correspond to any actual person, and you can train AI models without privacy liability.

The approach is gaining serious traction. Gartner estimated that by 2026, synthetic data would account for 60% of AI training data, up from under 5% in 2021. We’re not quite at 60%, but adoption is accelerating rapidly.

Why Synthetic Data Matters

Privacy by design. If your training data never contained real personal information, data breaches, leaks, or unauthorised access can’t expose anyone’s sensitive information. This is particularly valuable for healthcare, financial services, and government applications where data sensitivity is extreme.

Regulatory compliance. Many jurisdictions restrict cross-border data transfers or require explicit consent for AI training. Synthetic data sidesteps these constraints—it’s not personal data under most definitions, so regulations like GDPR don’t apply the same way.

Data scarcity problems. Some scenarios you need to train models for are rare in real data. Fraud detection needs examples of fraud, but fraud represents <1% of transactions. Medical diagnosis models need examples of rare diseases. Synthetic data can generate balanced training sets with adequate representation of edge cases that real data doesn’t provide in sufficient volume.

Cost and access. Acquiring real-world training data often requires complex agreements, data cleaning, and ongoing compliance obligations. Generating synthetic data can be faster and cheaper once the generation pipeline is established.

How Synthetic Data Generation Works

The technical approaches fall into several categories:

Rule-based generation. Define the rules and distributions that govern your data, then generate samples algorithmically. For simple structured data (customer age distributions, product purchase patterns), rule-based generation works well and is transparent. The downside is that human-defined rules often miss subtle patterns present in real data.

GANs (Generative Adversarial Networks). Two neural networks—a generator and discriminator—compete. The generator creates synthetic samples; the discriminator tries to distinguish them from real data. Through this adversarial process, the generator learns to create increasingly realistic synthetic data. GANs can capture complex patterns but require real data for initial training and can be unstable.

Variational Autoencoders (VAEs). These learn compressed representations of real data, then generate new samples from those learned representations. VAEs produce synthetic data that maintains statistical properties of the original without copying specific examples.

Foundation model-based generation. Large language models can generate synthetic text data. Diffusion models generate synthetic images. These approaches produce highly realistic outputs but require substantial compute resources.

The Privacy Catch

Synthetic data is only as private as the generation process. If you train a GAN on real customer data to generate synthetic customer data, and the GAN memorises specific training examples, the synthetic data could leak real information.

This isn’t hypothetical. Research has demonstrated that GANs and other generative models can memorise and reproduce training samples, particularly unusual or outlier examples. If your training data included a single person with a rare combination of characteristics, the model might generate synthetic samples that are effectively identical to that person’s real record.

Differential privacy addresses this by adding controlled noise during training to prevent memorisation. This mathematically limits how much information about any individual training example can leak into the model. The tradeoff is that differential privacy reduces data quality—more privacy means less accurate synthetic data.

Some organisations working in this space, like specialists in business AI solutions, are helping Australian companies implement synthetic data generation with appropriate privacy safeguards. The technical and regulatory considerations are complex enough that most organisations need specialist guidance to get it right.

Where Synthetic Data Works Well

Tabular structured data. Customer databases, transaction logs, sensor readings, and similar structured formats work well with synthetic generation. The statistical relationships are relatively straightforward, and validation is clear—does the synthetic data have similar distributions and correlations to real data?

Software testing and development. Developers need realistic test data. Synthetic data provides this without using production data in development environments, reducing compliance burden and breach risk.

Training and demonstration. When onboarding new staff or demonstrating systems to prospects, synthetic data allows using realistic examples without exposing actual customer information.

Augmentation for imbalanced datasets. Combining real data with synthetic examples of underrepresented categories creates more balanced training sets. This is particularly valuable for fraud detection, rare disease diagnosis, and anomaly detection where real examples are scarce.

Where Synthetic Data Struggles

Complex unstructured data. Generating synthetic medical images, handwritten documents, or video footage that maintains clinical or forensic validity while being truly synthetic is extremely difficult. The subtle patterns that matter (early signs of disease in medical imaging, document fraud indicators) are hard to replicate synthetically.

Temporal and sequential patterns. Time-series data with complex autocorrelation, seasonal patterns, and regime changes is difficult to generate synthetically with full fidelity. Financial market data, sensor streams from industrial equipment, and longitudinal health records all have temporal complexity that simple generation methods miss.

Rare events and anomalies. The exact scenarios you most need to train models for—fraud, equipment failures, security breaches—are the hardest to generate synthetically because they’re rare, complex, and not well characterised. Synthetic examples of anomalies often end up being too clean and not representative of real-world messiness.

Validation challenges. How do you know your synthetic data is good enough? If it’s too similar to real data, it might not be providing privacy protection. If it’s too different, models trained on it won’t perform well on real data. Finding the right balance requires extensive validation that many organisations underinvest in.

The Regulatory Picture

Regulators are still figuring out how to treat synthetic data. The Australian Office of the Information Commissioner has indicated that synthetic data may not be personal information if sufficiently de-identified, but hasn’t provided definitive guidance on what “sufficiently de-identified” means for synthetically generated data.

The EU’s GDPR contains similar ambiguity. Synthetic data that can’t be linked to identifiable individuals isn’t personal data—but proving that linkage is impossible requires rigorous demonstration.

This regulatory uncertainty creates risk. Organisations adopting synthetic data now are making assumptions about compliance that may not survive regulatory scrutiny as frameworks mature. Conservative approaches include treating synthetic data as if it were personal data until regulators provide clearer guidance, which somewhat defeats the purpose.

Practical Adoption Strategy

For organisations considering synthetic data:

1. Start with low-risk use cases. Software testing and training environments where the consequences of inadequate privacy protection are minimal. Learn the techniques and validate quality before moving to high-risk applications.

2. Maintain rigorous separation. Real data used for synthetic generation should be kept in secure environments with access controls. Synthetic data should be generated in ways that prevent memorisation and information leakage.

3. Validate extensively. Compare synthetic data statistical properties to real data. Test whether models trained on synthetic data perform adequately on real data. Check for memorisation by looking for exact or near-exact matches between synthetic and real records.

4. Document everything. Regulators will eventually ask how your synthetic data was generated, what privacy protections were applied, and how you validated non-identifiability. Maintain comprehensive documentation of the generation process.

5. Combine approaches. Synthetic data works best as part of a broader privacy-preserving strategy that includes differential privacy, federated learning, and traditional de-identification techniques.

Synthetic data isn’t a magic solution to AI’s privacy challenges. It’s a useful tool that works well for specific applications while introducing new complications around quality validation and regulatory compliance. Understanding where it helps and where it creates new problems is essential for adopting it successfully.