Seventy-five percent of enterprises are projected to use generative AI to create synthetic data by the end of 2026, according to Gartner — a jump from under 5% in 2023. The reason is simple: synthetic data promises exactly what every data team wants — volume, statistical realism, and speed — without the friction of asking approval to use real customer data. The problem lives in the second half of the sentence nobody finishes: without the friction, but not necessarily without the risk.
The most common mistake isn't using synthetic data. It's assuming that generating it solves the privacy and compliance part of the project in one shot. Brazil's LGPD doesn't exclude synthetic data from its own scope by definition — it excludes data that is truly anonymized, with the explicit caveat that it stops counting as anonymous if it can be reversed using reasonably available means. Recent research shows that caveat isn't theoretical.
The symptom: synthetic data became a synonym for safe data
The pattern repeats: the data team generates a synthetic version of a sensitive dataset, runs the tool's standard privacy metric, sees the "safe" stamp on screen, and hands the result to QA, to an external partner, or to a training pipeline — without checking whether the generator was tested against a real attack, without asking whether that specific use required more rigor than a statistical distance metric can guarantee.
LGPD's Article 12 is clear about where that assumption breaks: anonymized data only leaves the law's scope when the anonymization process can't be reversed with technical means reasonably available at the time of processing. Generating synthetic data from a real dataset is, by definition, an anonymization process like any other — subject to the same test, not to an exception regime just because the method uses generative AI instead of traditional masking.
Brazil's data protection authority (ANPD) has already shown it treats the line between synthetic content and personal data seriously, not as a gray zone. In Technical Note 1/2026, the agency treated AI-generated synthetic content that identifies a real person as personal data subject to LGPD — the concrete case was a deepfake, but the principle behind it applies to any artificial output that still carries enough information to point back to a real person. "It was generated by AI" isn't, by itself, a defense.
Where synthetic data really speeds a project up
When the use case is well defined, synthetic data solves a real bottleneck — and the speed gain is genuine, not vendor hype.
- Development and test environments without an approval queue. Engineering teams wait weeks for an approved, masked version of the production database. Synthetic data generated from the schema and statistical distribution removes that queue — the team tests against something statistically similar without touching any real customer record.
- Reinforcing rare classes in model training. Fraud, high-value churn, critical equipment failure — rare events the real data doesn't have in enough volume to train a model well. Synthetic data generated specifically for those minority classes improves accuracy without waiting years for collection.
- Sharing across squads and with an external partner. An integration partner or a third-party consultancy needs realistic data to validate an API or a report — without exposing the real customer base to a new risk perimeter.
- Sales demos and proofs of concept. Selling a data product or presenting a dashboard to a prospect requires volume and visual realism, not another customer's real records showing up on screen by accident.
Across all four cases, synthetic data replaces real data that only needed to look real, not to be traceable back to a specific individual afterward.
Where synthetic data fools whoever only looked at the privacy metric
The blind spot appears when the statistical fidelity that makes the data useful is the same fidelity that makes it reversible. A generator overfitted to the training data learns patterns that are too specific — including specific rare individual records — and reproduces that learning in the synthetic output in a way an attacker can exploit.
A study presented at the FASE 2026 academic conference tested exactly this gap: synthetic generation methods that passed the market's standard privacy metrics still leaked information under membership inference and reconstruction attacks, recovering a real fraction of the original records. The metric measured statistical distance between distributions; the attack estimated the real distribution behind it. Those are different questions, and passing the first doesn't answer the second.
Synthetic data that passes the privacy metric and fails the reconstruction attack isn't anonymous — it's anonymous until someone tests it.
That's exactly the "reversal with reasonably available means" that LGPD's Article 12 uses as its criterion. If an inference attack published at a 2026 academic conference can reconstruct part of the original dataset, that means is already reasonably available — which means synthetic data generated by that method never stopped being personal data in the eyes of the law, even with the tool's "anonymized" stamp.
The second blind spot is subtler: quality dropping generation after generation when a model is repeatedly retrained on mostly synthetic data, without periodically reintroducing real signal — it learns to reproduce its own biases instead of the pattern that existed in the original dataset. The same logic we've argued about clean data — quality is always relative to the use case, never an absolute standard applies to synthetic fidelity: good enough to test a signup screen can be bad enough to train a credit risk model.
Five questions before approving synthetic data in production
None of the five requires buying new tooling — they require deciding the scope before generating the data, not after distributing it.
- What exact use will it serve — internal testing, model training, or externally shared data? Tolerable risk changes completely across the three. Internal testing tolerates more fidelity and less scrutiny; data leaving the company demands the opposite.
- Was the generator audited against reconstruction and membership inference attacks — or only against a statistical distance metric? If it's only the latter, the privacy assessment is incomplete, not wrong by definition.
- Would this synthetic data be re-identifiable with the means reasonably available to whoever receives it? That's the question LGPD's Article 12 already asks — and it's the one that decides whether the data is still personal, regardless of the label the tool gave it.
- Does the fidelity cover the edge case the test or training needs to capture, without replicating the rare record that's also the most identifiable one? Fidelity and privacy pull in opposite directions; the decision needs to name which one weighs more for this specific use.
- Is there a plan to periodically reintroduce real data, avoiding degradation across successive generations of training on synthetic data alone? Without that plan, the model learns to reproduce its own biases instead of the real pattern the original dataset carried.
The second and third questions are the ones most teams skip — and they're exactly the ones the governance checklist for any AI pilot using real data already covers before the first prompt: map what goes in, check the legal basis, document it in writing. Synthetic data doesn't replace that discipline — it just moves which step the right question shows up in.
The right question isn't whether you can use it — it's which synthetic data for which use
Synthetic data isn't a compliance shortcut, and it isn't a trap by definition. The costly mistake isn't at either extreme — not rejecting the technology out of fear, not treating it as automatic exemption from privacy law because "it's not real data." It's skipping the step of naming, in writing, exactly which use that synthetic data was approved for, and with what level of privacy audit behind it.
A company that treats synthetic data as an accelerant for testing and training, with declared scope and real reversibility auditing, gets exactly the speed Gartner projects for most of the market by 2026. A company that treats the "synthetic" label as automatic synonym for "no risk" finds out the difference — as always — at the first incident, when someone asks whether that data could really never be reversed.
Questions that keep coming back
To close, the most common questions about synthetic data, privacy, and LGPD.
What is synthetic data and what is it used for?
Synthetic data is artificially generated records — today, mostly via generative AI — that reproduce the statistical patterns of a real dataset without containing any real record from it. It serves three main uses: speeding up test and development environments without waiting for masking approval, reinforcing rare classes in model training (fraud, churn, critical failure), and enabling realistic data sharing with an external partner or in a sales demo, without exposing the original customer base.
Does synthetic data exempt a company from LGPD?
Not automatically. LGPD only excludes truly anonymized data from its scope — and Article 12 is explicit that data stops counting as anonymous if it can be reversed with technical means reasonably available at the time of processing. Generating synthetic data is an anonymization process like any other, subject to the same test. 2026 research already showed synthetic generation methods approved by standard privacy metrics that still allowed attackers to reconstruct a real part of the original dataset — which legally keeps them within personal data scope.
Is synthetic data safe against leaking real information?
It depends on the method and how it was audited, not on the "synthetic" label itself. A generator overfitted to its training data learns patterns from rare individual records and can reproduce them in the output in a way that a membership inference or reconstruction attack can exploit — even when the tool's standard privacy metric classifies the result as safe. The metric measures statistical distance between distributions; the attack estimates the real distribution behind it. Without auditing against both attack types, "passed the metric" doesn't equal "is safe."