When the real data is locked behind privacy law, synthetic data is the way in, as long as you’re honest about what it does and doesn’t guarantee.
Why can’t you just use the real data?
In medtech and healthcare, the most valuable data is the most restricted. Patient records, clinical histories, anything tied to a real person sits behind privacy law (the Australian Privacy Act, GDPR, HIPAA) plus internal governance, ethics approvals and contractual limits on what you can move where.
So teams hit a wall. The data exists, but you can’t combine it across systems, can’t share it with a vendor or a research partner, can’t move it across a border, and often can’t get access approved inside your own organisation without months of process. Your model waits while the data sits locked in a room you’re not allowed to enter.
Is synthetic data actually private?
Done properly, yes, and this is the core of why it’s useful. Synthetic data is generated, not copied. You learn the statistical structure of the real data, then generate new records that carry the same patterns without being any real person’s data. There’s no individual in the synthetic set to re-identify, because none of them are real.
But “done properly” is doing real work in that sentence. A careless generator can memorise and regurgitate real records, quietly leaking the very data you were trying to protect. So the honest position isn’t “synthetic data is automatically private”. It’s “well-made synthetic data can be private, and you should measure it, not assume it.” Re-identification risk is something to test for, not wave away.
Will a model trained on synthetic data still work?
This is the fidelity question, and it’s the right one to be sceptical about. Synthetic data is only worth using if it behaves like the real thing, if the relationships a model needs to learn survive the trip from real to synthetic. Good synthetic data preserves those relationships; bad synthetic data smooths them away and gives you a model that’s confident and useless.
Two things make synthetic data genuinely valuable here, beyond privacy. It can balance datasets, amplifying the rare fraud case or the rare condition that real data barely contains, and that a model would otherwise never learn to catch. And it can cover edge cases you couldn’t ethically or practically collect. The test is always the same: does a model trained on it perform on real data? That’s measurable, and it’s the number that matters.
Does synthetic data make you compliant?
Short answer: no. This is the claim to be careful about, and the one vendors most often overreach on. Synthetic data doesn’t hand you a compliance certificate. What it does is reduce your exposure: less real personal data in play means a smaller attack surface, fewer copies of sensitive records to secure, and safer paths for sharing and collaboration. That can support your obligations under regimes like the Privacy Act, GDPR and HIPAA, but whether and how it does depends on your jurisdiction, your use case, and how the data was generated.
The responsible framing is “reduces exposure and enables safer data use,” not “compliant.” Anyone selling you the second version is selling you risk.
How do you validate synthetic data before you trust it?
Treat synthetic data like any other engineered component: it doesn’t ship until it’s tested, on two axes.
- Fidelity. Does it behave like the real thing? Compare distributions, correlations and, most importantly, downstream model performance on real data. If a model trained on synthetic data holds up on real data, the fidelity is real.
- Privacy. Can anyone recover a real individual from it? Measure re-identification and memorisation risk explicitly, rather than assuming generation equals anonymisation.
Only data that passes both is worth building on. That’s the whole discipline: fidelity without leakage. Realistic enough to train on, private enough to share, and validated on both counts before you trust it.
Get that right and the locked room stops being a wall. You don’t need to get into it; you can build a faithful copy of what’s inside, and leave the real records where they belong.
Privacy-safe, validated on both fidelity and re-identification risk: explore ALTDATA Synthetic Data.