Synthetic data is generated data, not a guarantee of anonymity. Before choosing a platform, define the task and release context, then test privacy risk, task-specific utility, relational behavior, integration, and operational scale against the same acceptance criteria. A vendor’s claim that data is “synthetic” or “privacy-preserving” is not a substitute for evidence about your data and threat model.
What is synthetic data, and is it really private?
Synthetic data is generated to resemble characteristics of source data, often so teams can develop, test, analyze, or share data without distributing the original records. That resemblance can be useful, but it does not by itself establish that individuals cannot be identified or that sensitive information cannot be disclosed.
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The National Institute of Standards and Technology (NIST) explains that some synthetic-data methods use differential privacy, which provides a mathematical privacy guarantee, while many other methods do not provide differential privacy or another formal privacy property. NIST’s May 3, 2021 explainer also cautions that accuracy can be difficult to achieve and that, for some tasks, purpose-built differentially private analyses may be more suitable than a synthetic-data workflow.
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Do not assume that generated records can never reproduce source values. In its documentation for AWS Clean Rooms synthetic output, AWS warns that literal source values—including personally identifiable information (PII)—may appear in generated data. AWS advises reviewing values associated with only one person and describes possible mitigations such as truncating high-precision values or replacing uncommon categories. This is a warning about that documented feature; it should not be treated as a universal description of every synthetic-data tool.
How should an enterprise decide whether to release or use synthetic data?
Start with the decision the data must support. A dataset suitable for software testing may not be suitable for external research, model development, or public release. The intended users, access controls, downstream uses, and consequences of disclosure all affect the acceptable risk.
NIST SP 800-188, published September 14, 2023, treats de-identification as a risk-management decision rather than a matter of simply removing identifiers. It recommends setting objectives, assessing disclosure risks, choosing a sharing model, considering oversight, establishing measurable performance levels, and conducting re-identification studies where appropriate. The sharing models it discusses include publishing synthetic data, publishing de-identified data, providing a query interface, or using a protected enclave. NIST cautions that “not all tools that merely mask personal information provide sufficient functionality for performing de-identification.”
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- Define the use: Identify the specific analysis, development, testing, or sharing purpose and what the recipient must be able to do.
- Set the release boundary: Decide who can access the data, where it will be stored, whether it can leave the organization, and whether a controlled interface or enclave would meet the need with less exposure.
- Agree on evidence: Set measurable privacy and utility criteria before comparing tools. Include review and approval roles, documentation, and conditions that trigger reassessment.
- Test the proposed release: Evaluate risks and utility using the intended data, recipient context, and downstream task rather than relying solely on a vendor’s general product description.
For financial-services teams, the Financial Conduct Authority’s August 19, 2025 report offers non-exhaustive governance considerations that may complement existing frameworks for conventional data and models; the FCA expressly says the report is not guidance. Its Synthetic Data Expert Group, established in March 2023, brings together 20 experts, and its first report examined six financial-services use cases. The UK Statistics Authority’s guidance, published January 29, 2025, provides an ethics checklist and resource for synthetic-data use in research, analysis, and statistics.
How do you test synthetic-data quality?
“Statistically similar” is too vague to serve as an acceptance criterion. Translate the intended use into tests: which distributions, correlations, edge cases, constraints, and downstream outcomes must the generated data preserve? A dataset can match broad distributions yet fail on a rare but important case or perform poorly in the target application.
NIST’s PETs Testbed describes evaluating fidelity, utility, and privacy together. It notes that privacy-preserving releases can introduce artifacts or bias, so quality should not be assessed in isolation from the privacy settings and release design. Its Collaborative Research Cycle page, updated September 22, 2026, reports more than 500 de-identified excerpts as NIST’s benchmark artifacts.
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- Fidelity: Compare the distributions, correlations, ranges, missingness, and other properties that matter to the use case.
- Utility: Run the actual downstream task—such as a test suite, analysis, or model-development workflow—and set an acceptable outcome in advance.
- Privacy: Test the disclosed threat model, including the risks of rare records and inference, and document the limits of the assessment.
- Representativeness: Check whether important groups, uncommon categories, and edge cases are retained or distorted in ways that affect the intended task.
- Repeatability: Record settings and inputs so the team can determine whether a new run or configuration changes privacy, quality, or operational results.
Use one representative dataset and a written scorecard for every shortlisted vendor. Keep acceptance thresholds tied to the intended use; there is no single quality score that establishes fitness for every enterprise task.
What should you compare in enterprise synthetic-data platforms?
Evaluate candidates against a shared task, data schema, and acceptance plan. Product descriptions establish what vendors document, not independent performance, security, or suitability for your environment.
| Option | Documented workflow and integration | Privacy and quality considerations | Buyer checks |
|---|---|---|---|
| Snowflake | Snowflake documentation describes generating data from source tables with matching column names and types and similar statistical properties, for testing or sharing. Users can designate join keys to create consistent artificial values across tables in a single run. The feature requires Enterprise Edition or higher. | Snowflake describes approximate distributions and correlations. Its documentation includes categorical and non-categorical string rules; the effects of those rules on a particular dataset are not established here. | Confirm edition and feature availability in your account. Test the string handling, schema constraints, joins, and task-specific utility with representative data. |
| AWS Clean Rooms | AWS documents synthetic output for ML input channels, with privacy-level (epsilon) and threshold settings. | AWS warns that literal source values, including PII, may appear in generated output. The documentation does not establish that this feature is suitable for every release context. | Inspect outputs for source-value reproduction and rare values; evaluate settings and mitigations against your threat model and release rules. |
| SDV Enterprise | SDV describes a licensed Python SDK for synthesizing interconnected tables, with deeper preprocessing and customization, data-source integration, and enterprise-wide deployment. | These are vendor-described capabilities; independent comparative benchmark results are not established here. | Validate the required source connectors, deployment pattern, relational behavior, and customization using your own pipeline and acceptance tests. |
For every candidate, compare privacy evidence, schema and relational behavior, access controls, lineage, deployment, data residency, automation, support, and licensing. Ask what happens to free-text fields, uncommon categories, constraints, and records that do not fit expected patterns. Confirm availability and contractual terms directly with the vendor before making a decision.
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Can synthetic data preserve joins across tables?
It can, but the answer depends on the product and workflow. Snowflake documents consistent artificial join-key values across tables when users designate join keys for a single generation run. SDV Enterprise describes synthesis for complex interconnected tables. Those descriptions do not establish that every tool preserves all referential constraints or that either approach will fit a particular schema.
Include a relational test in the pilot: generate the related tables together, verify key uniqueness and referential integrity, and run the joins and queries used by the downstream application. Check what happens when the workflow is rerun, when tables have uncommon relationships, and when the source includes nullable or constrained fields. Require evidence for the behavior your application depends on rather than inferring it from a general claim about multi-table support.
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How do you evaluate integration and scale?
Integration is more than whether a connector exists. Establish whether generation runs in your warehouse or cloud environment or requires exporting data, and check how the process fits identity and access controls, lineage, deployment, data residency, and pipeline automation. A workflow that moves source data across a boundary may not meet the same governance requirements as one that operates within an approved environment.
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Scale should be measured in a buyer-defined pilot, not inferred from words such as “enterprise” or “scalable.” Record the source and output sizes, run time, repeatability, concurrency, support needs, and operational effort for the workload you expect to run. The cited product materials do not establish comparative performance benchmarks, current pricing, security certifications, or data-residency terms.
- Choose representative data and workload: Include the schemas, table relationships, data volume, and downstream tasks that reflect the intended deployment.
- Run the same acceptance tests: Use equivalent criteria for privacy, utility, schema fidelity, join behavior, operational controls, and output inspection across shortlisted options.
- Measure the operating process: Record setup effort, execution time, rerun behavior, concurrency, and any required export or manual steps.
- Review the evidence and terms: Confirm current feature availability, licensing, contractual conditions, support arrangements, and deployment constraints directly with each vendor.
- Approve and monitor: Document owners, approvers, intended users, release criteria, and a cadence for reassessment when data, settings, threat assumptions, or use change.
What should a vendor evaluation deliver?
End the evaluation with a documented decision for the specific use—not a general declaration that one platform is “best.” The decision record should connect the chosen workflow to the purpose, privacy evidence, measured utility, integration design, operating requirements, and release controls.
- A defined intended use, audience, and sharing model.
- A documented threat model, privacy claim, test evidence, and known limitations.
- Task-specific utility thresholds and results, including relevant edge cases and downstream outcomes.
- Verified schema, constraint, and relational behavior.
- Integration and deployment details, including access control, lineage, data movement, and residency requirements.
- Pilot measurements for scale and operations, plus confirmed licensing and support terms.
- Named owners and approvers, retained review records, release criteria, and reassessment triggers.
These criteria make the decision auditable and keep “synthetic” from being treated as a blanket substitute for privacy review or production-data validation.
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