Synthetic data is generated data. It's produced by a model or algorithm trained on real examples, then used in place of those real examples to train or test other systems. The pitch is compelling: remove the privacy risk, sidestep the data-collection bottleneck, and generate as many labelled examples as you need on demand. Australian enterprises dealing with health records, financial transactions, and personal information are understandably interested. The Privacy Act reform process has made data minimisation a real operational concern, not just a compliance formality.
But synthetic data is not a clean substitute for real data. It carries its own failure modes, and the teams most likely to get burned are those who treat it as a drop-in solution without understanding what it actually preserves and what it discards.
What synthetic data actually does well
The strongest use cases share a common trait: the synthetic data fills a gap that real data cannot fill affordably or safely.
Training data augmentation is the most mature application. A team building a document classification model might have 2,000 labelled examples but needs closer to 20,000 to get acceptable performance. Generating synthetic variants, paraphrasing, back-translation, and noise injection can close that gap without collecting more sensitive documents. Computer vision teams have used this for years: synthetic images of product defects, vehicle positions, or medical scans provide coverage for edge cases that real datasets rarely include.
Privacy-preserving data sharing is the second strong use case. When an organisation needs to share training data with a vendor, contractor, or research partner, passing a differentially private synthetic dataset instead of the real one limits exposure. This isn't a perfect shield. Membership inference attacks can still extract information about individuals if the synthetic generation is poorly configured. But it raises the cost of extraction enough to satisfy many regulatory frameworks, including the Australian Privacy Principles as they currently stand.
Testing and development environments benefit cleanly. Synthetic data that statistically mirrors production data lets developers and testers work in realistic conditions without exposing live customer records. This is one case where the technique's limitations are largely irrelevant: you don't need the synthetic test data to be perfect, you need it to catch obvious regressions.
Where synthetic data breaks down in practice
Model collapse is the sharpest failure mode and the one that has attracted serious research attention. When synthetic data is fed back into the generation process iteratively, the output distribution narrows. Rare but real patterns are lost first. After several rounds, the model trained on synthetic outputs starts to behave as though those rare patterns never existed. For enterprise AI teams working in domains where outliers matter, like fraud detection or clinical decision support, this is a serious degradation.
Distribution shift is subtler. A generative model learns the statistical structure of training data, but real-world data distributions change. If the synthetic data was generated from 2023 transaction records, it will reflect 2023 behaviour. A fraud model trained on it may miss attack patterns that emerged in 2025. Real data at least has a timestamp you can reason about. Synthetic data obscures its provenance.
Spurious correlations get amplified. Real datasets contain noise that, paradoxically, helps models generalise. Synthetic data generation tends to smooth that noise, reinforcing the strongest correlations in the source data. If the source data contained a biased correlation (say, a demographic variable correlated with loan default due to historical lending policy rather than creditworthiness), the synthetic data will encode that bias with higher fidelity than the original. Teams working on AI bias in enterprise systems often find that synthetic data made the problem worse, not better, because it crystallised the bias that should have been corrected before generation.
Evaluation contamination is a practical failure that's easy to stumble into. If you generate synthetic training data and synthetic test data from the same generator, the test set no longer measures how well your model handles reality. It measures how well your model learned the generator's patterns. Benchmark numbers look good. Real-world performance is poor.
How Australian enterprises should think about this
The decision to use synthetic data should start with a clear statement of which problem it solves. If the answer is "we don't have enough data," synthetic augmentation is worth evaluating, with a holdout set of real examples reserved for validation. If the answer is "we can't share real data with a vendor," synthetic generation with differential privacy is a reasonable approach, but the privacy budget needs to be set deliberately, not left at defaults.
Quality validation is non-negotiable. At minimum, teams should compare the marginal distributions of real and synthetic datasets, test for correlation preservation, and run a train-on-synthetic, test-on-real benchmark. If performance on real held-out data is significantly below performance on synthetic test data, the synthetic distribution is drifting from reality in ways that matter.
Governance documentation should record the provenance of synthetic datasets. Which real data was used to train the generator? What generation method was used? What privacy budget was applied? What validation was done? Without this, teams lose track of what their models were actually trained on, which makes incident investigation and AI model versioning significantly harder when something goes wrong in production.
The Australian AI Safety Standard, which the government has been developing through 2026, includes provisions around data quality and traceability. Synthetic data pipelines that aren't documented will be difficult to audit against those requirements.
Tooling worth knowing
Three open-source libraries cover most enterprise use cases. SDV (Synthetic Data Vault) handles tabular, relational, and time-series data with multiple generation backends, including CTGAN and Gaussian copulas. It's the most commonly used in Australian enterprise data science teams. Gretel.ai offers a managed service with differential privacy built in, useful when sharing across organisational boundaries. For text and document use cases, most teams build on top of general-purpose language models using prompt-based augmentation, which is flexible but harder to validate for distribution fidelity.
None of these tools removes the need for real validation data. The output of any synthetic generation pipeline should be tested against a real holdout set before it enters a training workflow. That discipline is the difference between synthetic data as a genuine capability and synthetic data as a confidence trap.
Teams that have invested in responsible AI practices are better placed to use synthetic data well, because the governance habits that make responsible AI operational are the same ones that catch synthetic data failures before they reach production.