Synthetic data can reduce how much real information needs to move. It still has to prove that it is useful for the task and does not expose its source.
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?
Not by default. Synthetic data is generated rather than copied: a system learns useful structure from source data and creates new records for a defined task. That can reduce the number of real records being moved or shared.
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 can make synthetic data valuable here, beyond privacy. It can balance datasets, adding rare conditions that the available data barely represents. It can also cover cases that would be difficult or inappropriate to collect deliberately. The test is always tied to the intended use: does a model trained on it perform on real evidence?
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 “can reduce exposure and support safer data use,” not “compliant.” Legal and governance review still depends on the source data, generation method, intended use and jurisdiction.
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 is the discipline: preserve the signal the intended model needs, while testing that source records have not been memorised or exposed.
Get that right and a team may be able to develop or test a model while keeping fewer real records in motion. The legal and governance decision still belongs to the organisation using the data.
See the full workflow: explore ALTDATA Synthetic Data, from intended use through fidelity and privacy-risk checks.