Synthetic Data for AI Training: When It Works, When It Fails and How to Check It
Short answer: synthetic data for AI training works when it fills a specific gap in your real data, such as records you can't share for privacy reasons, rare events or edge cases, and when you check it against real data before training. It fails when it replaces real data instead of adding to it, when it is generated from thin or biased source data, or when nobody tests it for privacy leakage.
This guide covers what synthetic training data is, where it helps and where it breaks, the main generation methods, a pre-training checklist and a plan for a first project.
What is synthetic data?
Synthetic data is artificial data produced by an algorithm or a simulation. When it is generated from real data, a model learns the structure and statistical patterns of the original dataset and produces new records with similar properties, not copies of real rows.
A dataset can be fully synthetic (every value generated), partially synthetic (only sensitive fields replaced) or a hybrid of real and synthetic records. The main types are:
- Tabular: customer, transaction or sensor tables, where the hard part is keeping relationships between columns realistic.
- Text: support conversations, documents, question-and-answer pairs and instruction data for language models.
- Image and video: rendered or generated scenes for computer vision, often labelled automatically.
- Time series: payments, machine telemetry or vital signs, where order and timing matter.
In a Gartner Peer Community survey of 150 IT and data leaders who work with synthetic data (April 2023), 84% used text-based synthetic data, 54% image-based and 53% tabular. Most (63%) used partially synthetic data; fully synthetic datasets were less common.
When synthetic data helps
Synthetic data is most useful when real data exists but can't be used freely, or barely exists for the cases that matter.
- Privacy-locked data. Health, financial and HR records are often too sensitive to hand to developers, vendors or a cloud sandbox. A well-tested synthetic version lets teams build pipelines without touching real records.
- Rare events and class imbalance. Fraud, equipment failures and defects are rare by nature. Extra synthetic examples of the minority class give a model more to learn from, as long as you measure the result on real cases.
- Edge cases you can't safely collect. Simulation can produce scenes that are dangerous or expensive to capture, such as unusual lighting, weather or obstacles for a vision system.
- Language models. Synthetic text is now widely used in LLM development. NVIDIA reported that over 98% of the data used in its Nemotron-4 340B alignment process was synthetically generated.
- Testing and data sharing. Realistic fake records let developers test integrations without copying production data, and let partners or vendors explore a dataset before anyone shares the real one.
When synthetic data fails
Synthetic data vs real data is not an either/or choice, and most failures come from treating it as one. In the same Gartner survey, the most common challenges were a lack of real-world source data (51%), inherited bias (46%) and low-quality source data (41%). Synthetic data inherits the problems of the data behind it.
Model collapse
Research by Shumailov et al., published in Nature in July 2024, showed that when models are trained on recursively generated data, the tails of the original distribution (the low-probability events) disappear first, and the defects become irreversible. In their language-model experiments, keeping 10% of the original training data in each generation led to only minor degradation.
The lesson: add synthetic data to real data rather than replacing it, and label synthetic records so they aren't recycled into the next training run.
Missing the real-world long tail
A generator reproduces the patterns it sees most often. Rare combinations and genuine outliers are what it captures worst, and the European Data Protection Supervisor (EDPS) warns that synthetic data "may not cover some outliers that original data has". In fraud detection or safety monitoring, those outliers are often the whole point.
Privacy leakage
"Synthetic" does not automatically mean "anonymous". The ICO's guidance on synthetic data says whether it is still personal data depends on whether the personal information it was modelled on can be inferred from it. It also notes that some generation methods have been shown to be vulnerable to model inversion and membership inference attacks. Privacy-preserving synthetic data is something you test for, not something you assume.
Bias copied or amplified
The EDPS notes that synthetic data "may reflect the biases in original data". If a group is under-represented in the source, the generator has little to learn from and may represent it even less accurately. Rebalancing helps, but the EDPS also warns that manipulating data to make it fairer "might result in inaccurate data", so test every change.
How synthetic data generation works: the main methods
The right method depends on the data type, how much real data you have and the privacy guarantee you need.
- Rules-based generation. Data comes from business rules and schemas, with no real records involved. Good for software testing, but only as realistic as the rules.
- Statistical models, such as Gaussian copulas. The model learns each column's distribution and the correlations between columns, then samples new rows. Fast, explainable and a sensible first baseline for tables.
- GANs, such as CTGAN. A generator is trained against a discriminator that tries to tell real records from fake ones. CTGAN adapts this to tables that mix numeric and categorical columns. Often better at complex patterns, but slower and harder to debug.
- Variational autoencoders (VAEs). The model compresses records into a compact representation and samples new ones from it. TVAE is a version built for tables.
- Diffusion models. These learn to reverse a gradual noising process. Best known from image generation, with research versions for tabular data.
- LLM-generated text and instruction data. A strong language model writes examples, such as question-and-answer pairs or dialogues, which are then filtered and reviewed. This is a common way to build datasets for LLM fine-tuning; if you're unsure you need fine-tuning at all, read our guide to RAG vs fine-tuning.
- Simulation and 3D rendering for computer vision. Rendered scenes come with exact labels. Randomising textures, lighting and camera angles helps models trained on simulated images work on real ones.
Several of these can be combined with differential privacy, a mathematical guarantee that limits how much any single record can influence the output, usually at some cost to accuracy. For tables, publicly available Python libraries such as SDV (Synthetic Data Vault) and SDMetrics include several of these generators and checks; review the licence before commercial use.
How to check synthetic data before you train on it
A research paper by staff at the FCA, the ICO and the Alan Turing Institute frames validation around three properties: fidelity (statistical similarity to the real data), utility (usefulness for a task such as training a model) and privacy (the risk that individuals can be re-identified). They pull against each other: the more similar synthetic data is to the real data, the higher the re-identification risk. Agree what "good enough" means for your use case first.
Fidelity
- Compare each column's distribution with the real data: shape, range and missing values.
- Compare relationships: correlations and pairs of columns that should move together.
- Confirm business rules hold: dates in order, totals that add up, valid codes.
- Check that rare categories and extreme values appear at realistic rates, not zero.
Utility
- Run train-synthetic-test-real (TSTR): train a model on synthetic data, test it on held-out real data and compare the score with the same model trained on real data.
- When augmenting, compare real-only with real-plus-synthetic, focusing on the minority classes and edge cases you set out to fix.
- Check performance for each customer group, not just the overall average.
Privacy
- Distance to closest record (DCR): measure how close each synthetic row is to its nearest real row. Exact or near-exact copies are a red flag.
- Membership inference test: check whether an attacker could tell if a specific person's record was in the training data.
- Attribute disclosure: check whether sensitive values can be guessed from other fields.
- Outliers: review people who are unique in the source data. The ICO lists suppressing outliers and applying differential privacy as mitigations.
- Documentation: the EDPS recommends a privacy assurance assessment to confirm the synthetic data is not actual personal data.
A step-by-step plan for your first synthetic data project
- Pick one narrow problem with a measurable target, such as "improve recall on fraud cases" or "give a vendor a dataset without real customer records".
- Audit the real data. Review quality, gaps, bias and sensitive fields, confirm your legal basis, and set aside a real hold-out test set the generator never sees.
- Start with the simplest method that fits: a statistical model for tables, an LLM with filtering for text, a simulator for images.
- Generate a small batch and run the checklist before scaling up.
- Train on real plus synthetic data and compare against a real-only baseline on the hold-out set. If the metric you care about doesn't improve, stop.
- Label and version everything: synthetic records, generator settings and outputs, and keep synthetic output out of future source data.
- Monitor after deployment on live, real-world data, and regenerate when the real data drifts.
Compliance notes: GDPR and the EU AI Act
This is general information, not legal advice. Check your situation with your data protection officer or legal counsel.
- GDPR and UK GDPR. Data protection law doesn't apply to truly anonymous data, but synthetic data generated from personal data isn't anonymous by default. The ICO notes that generating it "may involve processing personal information", so the generation step has to comply with data protection law, and the output needs an identifiability assessment.
- EU AI Act, Article 10. For high-risk AI systems, Article 10 requires training, validation and testing data sets to be "relevant, sufficiently representative, and to the best extent possible, free of errors and complete in view of the intended purpose", and examined for possible biases. It makes no exception for synthetic data, so keep records of how yours was generated and checked.
- Bias detection. The Digital Omnibus on AI, in force since 27 July 2026, moved the bias-detection rule into a new Article 4a. It allows special categories of personal data to be processed to detect and correct bias only when that can't be done effectively with other data, "including synthetic or anonymised data".
- Timing. Under the Omnibus, high-risk rules for stand-alone (Annex III) systems apply from 2 December 2027, and for AI in regulated products (Annex I) from 2 August 2028.
For help mapping this to your own systems, our AI governance consulting team reviews data flows, documentation and controls.
How RixDigi approaches synthetic data projects
Our synthetic data generation services follow four stages. A baseline data audit profiles the real data, its gaps, biases and sensitive fields, and sets what the synthetic data must improve. Pipeline configuration selects and tunes the generation method for your data type and privacy requirements. Statistical validation tests fidelity, utility and privacy against real data before anything is used for training. Delivery and audit reporting hands over the dataset with documentation of how it was generated and tested, so your team and auditors can trace it.
Frequently asked questions
Is synthetic data as good as real data for training AI?
Not on its own. Synthetic data reflects what the generator learned from real data, so it rarely captures every rare case or real-world shift. It works best combined with real data and judged on a real test set.
Is synthetic data anonymous under GDPR?
Not automatically. If personal information from the source data can be inferred from the synthetic records, it may still be personal data. Test for re-identification risk and document the assessment before treating it as anonymous.
How much synthetic data should I add to my training set?
There is no universal ratio. Increase the share step by step, measure performance on real hold-out data each time, and stop when the metric you care about stops improving. Keep real data in every training round.
What is model collapse?
Model collapse is the loss of quality that happens when models are trained repeatedly on data generated by other models. Rare events disappear first and outputs become narrower over time. Keeping real data in the mix and labelling synthetic records helps prevent it.
Which tools can generate synthetic data?
For tables, Python libraries such as SDV offer statistical, GAN and VAE generators plus quality and privacy metrics. For text, teams usually prompt a large language model and filter the results. For images, simulation and 3D rendering tools or diffusion models are common.
Plan your synthetic data project with us
Considering synthetic data for a model that can't get enough real data? See our synthetic data and model training sets service or book a strategy call to talk it through with our AI engineering team.
Written by Aqib Rehman, Founder & Chief AI Officer at RixDigi. Aqib holds an MS in Artificial Intelligence from NUST and has spent more than 12 years in technology.
