Every AI program eventually hits the same wall: the data that would make a model useful is too sensitive to share, too rare to learn from, or too skewed to trust. Synthetic data, artificially generated records that mimic the statistical properties of real data, has moved from research curiosity to mainstream tool in response. Model builders use it to fine-tune language models, banks use it to test fraud systems, health researchers use it to share data across institutions, and even Swift trained its open-source address structuring model on synthetic data rather than personal data.
But synthetic data is not automatically private, not automatically accurate, and not a free substitute for real data. Used carelessly, it leaks the records it was meant to protect, erases the rare cases models most need to learn, or feeds a loop in which models learn from their own outputs and degrade. This guide explains when synthetic data is the right choice, how to evaluate fidelity, utility and privacy, and how to govern it as part of an AI-ready data platform.
Why Synthetic Data Is Back at the Top of the Agenda
Three forces have pushed synthetic data up the priority list in 2025 and 2026. First, generative AI made generation easy. Large language models can produce realistic text, dialogues, code and labeled examples in minutes, and tabular generators built on copulas, tree-based methods or deep generative models are available in open-source libraries and commercial platforms.
Second, demand for training and evaluation data exploded. Teams building agents, retrieval systems and domain-tuned models need instruction pairs, edge-case scenarios and evaluation sets that rarely exist in usable form. Third, privacy and AI regulation tightened. Data protection law in Europe, Asia-Pacific and North America limits how personal data can be reused, and the EU AI Act sets data governance requirements for high-risk systems. The original Act text explicitly referred to synthetic or anonymized data as alternatives to consider before processing special categories of personal data for bias detection; the Digital Omnibus on AI has since restructured that provision, so teams should check the current consolidated text, but the policy signal toward privacy-preserving alternatives remains.
What Synthetic Data Is Good For, and What It Is Not
Synthetic data is a family of techniques rather than one thing, and the use case determines the method and the evaluation bar.
Strong use cases
- Development and testing environments. Realistic but non-identifying data lets engineers build pipelines, test integrations and demo applications without copying production records into lower environments.
- Augmenting rare classes and edge cases. Fraud, equipment failures, safety incidents and unusual customer journeys are scarce by definition. Carefully generated examples can rebalance training data or stress-test a model.
- Evaluation sets for LLM applications. Generated questions, adversarial prompts and multi-turn scenarios help teams test retrieval and agent behavior at a scale manual writing cannot reach, provided humans review samples.
- Data sharing and collaboration. Sharing a synthetic version of a data set with partners, vendors or researchers can reduce exposure when the generator and outputs pass privacy testing.
- Instruction tuning and distillation. Using a strong model to produce task-specific training examples for a smaller model is now a standard technique, subject to model license terms.
Weak or risky use cases
- Replacing real data entirely for models that make consequential decisions about people, where subtle correlations matter and errors carry legal weight.
- Estimating real-world prevalence or performance. Synthetic data reflects the generator’s assumptions; it cannot tell you how often something actually happens.
- Public release without testing. Treating synthetic output as anonymous by default is the most common and most expensive mistake.
- Learning genuinely new patterns. A generator can only recombine what it learned from source data or from its own training. It will not discover the signal you do not have.
Synthetic data is a privacy and coverage tool, not a truth machine. Use it to protect, rebalance and stress-test real data, and keep real data in the loop for training anchors and final evaluation.
Privacy: Synthetic Does Not Mean Anonymous
Generators learn from real records, and models can memorize. If the source contains outliers, such as a patient with a rare condition or a customer with an unusual combination of attributes, the generator may reproduce something close enough to identify them. Whether synthetic data counts as anonymous under laws such as the GDPR depends on residual re-identification risk, not on the label.
Singapore’s Personal Data Protection Commission published a guide on synthetic data generation in 2024 that remains one of the most practical regulator documents on the subject. It frames privacy evaluation around concrete attacks:
- Singling out: isolating a record, often an outlier or unique combination of attributes.
- Linkability: combining synthetic data with other data sets to connect records to the same person.
- Inference: deducing sensitive attributes of real individuals from known attributes, compared against a baseline of people outside the data set.
- Membership inference: determining whether a particular person was in the source data, which matters most when membership itself is sensitive, such as a disease registry.
The PDPC guide recommends defining an acceptable risk threshold up front, running attack-based tests, iterating with mitigations such as generalization, outlier removal or noise when risk is too high, and managing residual risk through access controls, contracts and incident planning, including scenarios in which the generator model itself leaks.
Where differential privacy fits
Differential privacy (DP) offers a mathematical bound on how much any single record can influence the output, and DP-trained generators are the strongest technical protection available. NIST finalized Special Publication 800-226, Guidelines for Evaluating Differential Privacy Guarantees, in March 2025 to help organizations assess DP claims, including the trade-off between privacy and usefulness and the hazard that small groups need more noise to stay protected. The practical point: if a vendor claims its synthetic data is differentially private, ask for the parameters, the unit of privacy and how the guarantee was implemented, and evaluate the claim rather than accepting the label.
Quality: Measuring Fidelity and Utility
A synthetic data set that is perfectly private and useless is easy to build. The work is balancing privacy against usefulness, and that requires measurement on three levels.
- Integrity. Does the output respect the schema, data types, valid ranges, referential integrity and business rules in the data dictionary? Impossible values, such as discharge dates before admission dates, are an immediate red flag.
- Fidelity. How closely does the synthetic data match the source distributions and relationships? Compare univariate distributions, pairwise correlations and conditional distributions for key segments, not just global averages.
- Utility. Does it work for the intended task? The standard test is train-on-synthetic, test-on-real: train the same model on synthetic and real data and compare performance on a held-out real test set using the metrics that matter for the use case.
Two additional checks are worth adding. First, segment-level fidelity: generators often reproduce the majority well and smooth away minorities, which creates fairness problems downstream. Second, for LLM-generated text, diversity and factual grounding: generated examples tend to converge on similar phrasings and can introduce confident errors, so measure duplication, topic coverage and accuracy on sampled items reviewed by domain experts.
Model Collapse and the Feedback Loop Problem
A 2024 paper in Nature by Shumailov and colleagues showed that generative models trained recursively on data produced by earlier models suffer what the authors called model collapse: the tails of the original distribution disappear over generations. The finding drew wide attention because more and more web content is itself machine-generated.
Later research has refined the picture. A study by Kazdan, Schaeffer and co-authors found that collapse occurred when synthetic data replaced real data across generations, but that accumulating synthetic data alongside retained real data stayed stable in their experiments. The practical guidance for enterprises is consistent with both results:
- Keep an immutable, versioned store of real data and never let synthetic records silently overwrite it.
- Label synthetic records at the row or file level so they can be filtered, weighted or excluded later.
- Track generator lineage: which model, version, prompt or configuration and source snapshot produced each batch.
- Evaluate final models on real, held-out data, never only on synthetic test sets produced by the same pipeline.
- Watch for shrinking diversity, such as vanishing rare categories or repetitive text, as an early warning sign.
A Decision Framework: Should You Use Synthetic Data?
Before starting a synthetic data project, answer five questions honestly.
- What is the blocker? Privacy constraints, scarcity of rare events, class imbalance, cost of labeling, or lack of test data each point to different techniques. If the blocker is poor quality in the real data, synthetic data will copy the problem.
- What decision will the model influence? The higher the stakes, the more real data you need for training anchors and final validation.
- Who will receive the data? Internal developers under access controls, external vendors under contract and the public each require a different privacy threshold.
- Can you measure success? If you cannot define fidelity, utility and privacy metrics and acceptance thresholds, you cannot tell good synthetic data from bad.
- Are there simpler options? Masking, aggregation, federated approaches, secure enclaves or simply better access governance sometimes solve the problem with less risk.
Playbook: A Governed Synthetic Data Pipeline
Combining the PDPC approach with common enterprise practice gives a pipeline that fits inside an existing data platform.
- Define purpose and thresholds. Document the use case, recipients, acceptable re-identification risk and expected utility. Where personal data is involved, record this in a data protection impact assessment.
- Prepare and minimize source data. Select only the attributes needed, remove direct identifiers, generalize granular values and handle outliers deliberately. Keep a data dictionary.
- Hold out a control set. Split the source so part of it never touches the generator. It serves both utility testing and privacy attack baselines.
- Generate with an appropriate method. Statistical and tree-based methods suit many tabular use cases; deep generative models and LLMs suit complex or unstructured data; DP training is the choice when strong guarantees are required.
- Evaluate integrity, fidelity, utility and privacy. Automate the metrics and record results with the batch. Iterate when thresholds are missed.
- Approve, label and catalog. Publish the data set to the catalog with clear synthetic labels, lineage to source snapshot and generator, evaluation report and usage restrictions.
- Control access and monitor. Restrict access to source data and generator models, log usage, review risk periodically and before any wider release, and delete models and data when no longer needed.
A Worked Example: Synthetic Claims Data for a Fraud Model
Consider a hypothetical insurer that wants an external partner to help improve its claims fraud model. Real claims include personal and health-related information, and confirmed fraud cases are a small fraction of the data. Sharing raw records is off the table.
The data team defines two goals: a shareable data set for the partner to develop features and models, and augmented fraud examples to rebalance internal training. It minimizes the source to the attributes needed, generalizes dates into weeks and amounts into bands where precision is unnecessary, removes extreme outliers that would be easy to single out, and holds out a fifth of the records as a control set.
A tabular generator is trained and evaluated. Integrity checks catch impossible combinations of policy type and claim category, which are fixed with constraints. Fidelity looks good overall but poor for a small regional segment, which the team flags so the partner does not over-interpret it. Utility testing shows that a model trained on the synthetic data performs somewhat below one trained on real data, which is acceptable for feature exploration. Privacy tests for singling out, linkability and membership inference meet the agreed threshold after one iteration that adds noise to two quasi-identifiers.
The partner receives the synthetic data under a contract prohibiting re-identification attempts. Internally, augmented fraud examples are blended with real data, labeled as synthetic, and the final model is validated only on real held-out claims. Every artifact, from source snapshot to generator configuration and evaluation report, is cataloged.
Pitfalls to Avoid
- Assuming privacy by construction. Without attack-based testing, synthetic data can still identify people.
- Optimizing only for fidelity. The closer the output is to the source, the higher the leakage risk. Balance is the goal.
- Ignoring minority segments. Aggregate metrics hide poor performance for small groups, which then becomes a fairness issue in production.
- Losing track of what is synthetic. Unlabeled synthetic records eventually contaminate analytics and training sets.
- Overlooking license terms. Some model providers restrict using outputs to train competing models; check before using generated data for distillation.
- Skipping real-world validation. A model that looks strong on synthetic test data may fail on reality.
Outlook
Synthetic data will keep growing in importance through 2027. Model developers already depend on it for instruction tuning and reasoning data, physical AI and robotics rely on simulated environments, and enterprises are building synthetic evaluation suites for agents. Expect more regulator guidance on when synthetic data counts as anonymous, more standardized evaluation metrics, and tighter integration of generation into data platforms and catalogs. The organizations that benefit most will be the ones that treat synthetic data as a governed data product with lineage, evaluation and access control, not as a one-off workaround.
Conclusion
Synthetic data can unblock AI projects stalled by privacy, scarcity or imbalance, and it can make testing and collaboration safer and faster. It can also leak, distort and degrade when used without discipline. Start with a clear purpose and thresholds, measure integrity, fidelity, utility and privacy every time, keep real data as the anchor for training and validation, and label and catalog everything you generate. Done that way, synthetic data becomes a reliable part of an AI-ready data foundation rather than a new source of risk.
How Hanoi Technology Can Help
Hanoi Technology helps teams design governed synthetic data pipelines, with privacy testing, fidelity and utility evaluation and catalog lineage, as part of an AI-ready data platform.
Further Reading
- Guide on Synthetic Data Generation – Personal Data Protection Commission Singapore
- NIST finalizes guidelines for evaluating differential privacy guarantees – NIST
- AI models collapse when trained on recursively generated data – Nature (via RePEc)
- Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World – arXiv
- Article 10: Data and data governance – EU AI Act Service Desk
- ISO 20022: The Swift AI address structuring model – Swift