Enterprise teams are hitting a familiar wall with generative artificial intelligence (GenAI). You can’t scale value if the data you need is locked behind privacy rules, buried in legacy systems, too expensive to collect, or too rare to show up in meaningful volumes.
That’s where synthetic data stops being a research buzzword and starts behaving like strategy. When it’s done well, synthetic data gives you “data you can actually use” for model training, testing, and sharing, without hauling sensitive records into every experiment. When it’s done badly, it quietly poisons decisions, breaks compliance assumptions, and convinces you your model is ready when it isn’t.
This is the real conversation: not “synthetic vs real”, but how to use synthetic data to make GenAI safer, faster, and more governable without kidding yourself about risk.
What Synthetic Data Actually Is
Synthetic data is artificially generated data designed to reflect useful properties of real data, without being a record of real-world events. The simplest definition is that it’s created from seed data and preserves some of its statistical characteristics.
That’s the headline. The part people miss is the intent: synthetic data is generated to solve a data task, not to perfectly recreate reality. The Royal Society frames it as data produced by a purpose-built model or algorithm to support data science work.
So “good” synthetic data is not “fake data that looks real”. It’s data that is fit for purpose, measured against the job you need it to do.
How GenAI Creates Synthetic Data
Most synthetic data generation sits on a spectrum, and GenAI expands what’s possible across that range:
- Statistical synthesis: generates data based on distributions and correlations from the source dataset.
- Model-based synthesis: uses machine learning models (including deep generative models) to learn patterns and generate new rows, sequences, images, audio, or text.
- Scenario simulation: generates data from rules or simulations (common in engineering, robotics, and safety testing).
GenAI matters here because it can generate high-dimensional data that is hard to handcraft: conversations, medical notes, call transcripts, software logs, time series, images, and edge-case scenarios. It can also help transform “messy” inputs into safer training material by rewriting or restructuring content, which is increasingly relevant as organisations try to use data they already have without leaking personal details.
But there’s a governance catch: the more powerful the generator, the more disciplined you have to be about proving privacy protection and utility, not just assuming it.
Why Synthetic Data Is Showing Up in Enterprise AI Roadmaps
Synthetic data is not a niche workaround anymore. Gartner has been pushing synthetic data as a major trend in data science and machine learning, including a prediction that by 2024, 60 per cent of data used for AI could be synthetic in certain contexts. Their more recent data and analytics predictions also flag synthetic data governance as a failure point that can undermine compliance and model quality.
The enterprise pull is straightforward:
- Privacy and access constraints
Teams waste months negotiating access to sensitive datasets. Synthetic data can reduce exposure, speed up experimentation, and make cross-team collaboration realistic. - Data scarcity and imbalance
Real datasets often underrepresent rare events (fraud patterns, safety incidents, uncommon clinical outcomes). Synthetic data can increase representation, but only if you control for distortion. - Faster testing and validation
Synthetic datasets can be generated repeatedly, with controlled changes, to test model robustness and failure modes. - Safer data sharing
Regulated industries want to collaborate without transferring personal data. UK regulators and public bodies have been actively exploring synthetic data governance, utility, and privacy trade-offs in finance and health contexts.
None of that automatically makes synthetic data “anonymous” or “safe”, though. That’s where most programmes get sloppy.
Synthetic Data Does Not Automatically Equal Anonymous Data
A lot of marketing language implies synthetic data is a privacy silver bullet. Reality is more conditional.
The UK Information Commissioner’s Office (ICO) makes the core point that anonymisation is about reducing identifiability to a sufficiently remote level, and what counts as “effective” depends on context. That applies to synthetic data too. If your synthetic dataset can be linked back to individuals, or if it leaks sensitive attributes through rare combinations, you still have a problem.
This is why the conversation has shifted toward privacy risk assessment, not labels. The UK Financial Conduct Authority (FCA) discusses validation through three lenses: privacy, utility, and fidelity, and pushes a risk-based approach to privacy validation. FCA
If your programme can’t explain those three lenses in plain language, it’s not ready for production use.
Where Synthetic Data Helps GenAI Most
Synthetic data shines when it removes friction without weakening truth. The best enterprise use cases usually fit one of these patterns:
Supporting LLM applications without exposing sensitive records
If you’re building internal GenAI tools (customer service copilots, policy Q&A, case summarisation), you often don’t need raw customer records in every dev cycle. Synthetic conversations, synthetic cases, and synthetic logs can accelerate iteration while reducing exposure.
The discipline is in the “truth backbone”: you still need validated policy and process knowledge, plus controlled evaluation on real-world samples, to avoid building a confident liar.
Filling gaps and stress-testing edge cases
Teams love GenAI demos because they look competent under normal conditions. Then they hit rare inputs: ambiguous language, incomplete data, adversarial prompts, unusual combinations of facts.
Synthetic data can generate controlled stress tests at scale. It’s particularly useful for red teaming and safety testing because you can programmatically create “near-miss” cases that resemble real scenarios without being real individuals.
Improving data sharing across teams and partners
Synthetic datasets can unblock collaboration between analytics, product, and security, and can support external sharing in controlled programmes. FCA materials explicitly position synthetic data as a privacy-enhancing technology that can expand data sharing, while noting there are still open questions.
This is also where “good enough” synthetic data can still be valuable. If the goal is to test pipelines, dashboards, integrations, and permissioning, the bar is different than training a clinical model.
The Risks That Make Synthetic Data a Governance Topic
Here are the risks that shouldn’t blur together:
- Privacy leakage: synthetic data can still reveal sensitive information if the generator memorises, if the dataset is too small, or if rare combinations persist.
- Bias replication or amplification: synthetic data can reproduce historic bias, or intensify it if the generator overemphasises dominant patterns.
- False confidence: models may perform well on synthetic data but fail on real-world distributions.
- Model collapse and feedback loops: training repeatedly on model-generated data can degrade quality over time, a phenomenon demonstrated in peer-reviewed research on “model collapse”.
- Compliance mismatch: teams may assume synthetic data removes regulatory obligations, when the reality depends on identifiability and use context.
If those risks sound abstract, they usually show up as very practical failures: audit findings, poor generalisation, unexplainable model behaviour, and messy internal debates about what data the team is allowed to touch.
What The EU AI Act Signals About Data Discipline
For teams operating in, selling into, or influenced by EU regulatory expectations, it’s worth treating the EU AI Act as a direction-of-travel signal for data governance, even when your specific system isn’t classified as high-risk.
Article 10 focuses on data and data governance requirements for high-risk AI systems, emphasising training, validation, and testing datasets that are relevant, representative, and managed with appropriate governance.
Synthetic data can support those goals, but it doesn’t remove them. In practice, you still need to prove dataset suitability, document design choices, and show how you controlled bias and gaps. Synthetic data can help you do that more safely, but it can’t do it for you.
A Practical Framework For Using Synthetic Data With GenAI
Most synthetic data programmes fail because they start with the generator, not the decision. A more resilient approach is to treat synthetic data like a governed product.
1) Define the decision you’re supporting
Be blunt about the job:
- Are you training a model?
- Evaluating a model?
- Testing an application flow?
- Sharing data for research or vendor development?
- Simulating rare events?
This matters because the acceptance criteria for utility and risk are different for each.
2) Set non-negotiables before you generate anything
These are the guardrails that stop the programme turning into vibes:
- You must be able to explain the source data provenance and purpose (even if the output is synthetic).
- You must measure privacy risk, not assume anonymity.
- You must measure utility against the use case (not “it looks realistic”).
- You must validate on real-world samples before production decisions.
Those aren’t “nice to haves”. They’re the difference between synthetic data as acceleration and synthetic data as self-inflicted harm.
3) Validate with the “privacy, utility, fidelity” triangle
The FCA’s framing is useful because it forces trade-offs into the open. FCA
- Privacy: what is the risk of re-identification or sensitive attribute inference?
- Utility: does the synthetic data support the task (training, testing, analysis) at an acceptable level?
- Fidelity: how closely does the synthetic data reflect the relevant characteristics of the real dataset?
You’re not aiming to maximise all three. You’re aiming to optimise the triangle for the decision you’re making, and document why.
4) Treat synthetic datasets as versioned assets
If you can’t answer “which version of the synthetic dataset trained this model?” you are building future incident response work for yourself.
Version synthetic datasets like software artefacts: metadata, generation method, parameters, privacy evaluation summary, intended use, and expiry conditions.
5) Prevent synthetic-on-synthetic contamination
The model collapse research is the warning label: if you repeatedly train on generated outputs, you can distort the underlying distribution and lose long-tail behaviour.
A simple enterprise rule that prevents quiet decay is to enforce a minimum proportion of real-world evaluation data for every release, and to track whether synthetic data is being reused as seed data across generations.
Build Or Buy: What Actually Matters In Vendor Selection
A synthetic data platform is not “a tool that makes fake data”. You’re buying governance maturity.
When evaluating options, focus on:
- Can the vendor explain how they reduce memorisation risk and support privacy evaluation?
- Can they show how they measure utility for different downstream tasks?
- Do they support auditable metadata and dataset lineage?
- Can they integrate with your existing data governance and access controls?
If the demo is only “look how realistic this row looks”, it’s not an enterprise-grade conversation yet.
FAQs
Is synthetic data always compliant with privacy laws?
No. Synthetic data can still create privacy risk depending on how it’s generated, the size and sensitivity of the source dataset, and whether individuals could be re-identified or inferred. Regulators emphasise context-driven assessment for effective anonymisation.
Can synthetic data replace real data for GenAI training?
It can reduce dependence on real data and fill gaps, but replacing real data entirely is risky. Research on model collapse shows that recursively training on generated data can degrade model quality over time.
What’s the best way to validate synthetic data quality?
Use a structured approach that balances privacy, utility, and fidelity, aligned to the specific use case. The FCA’s work is a strong reference point for this validation mindset.
Does the EU AI Act mention synthetic data directly?
The Act’s data governance requirements focus on dataset quality and management for high-risk systems. Synthetic data can support compliance goals, but it doesn’t remove obligations around relevance, representativeness, and governance.
Final Thoughts: Synthetic Data Only Works When You Can Prove It
Synthetic data is becoming a core enabling layer for GenAI because it tackles the ugliest blockers: privacy, access, scarcity, and the time it takes to get anything approved. Done well, it lets teams move faster without turning risk into an afterthought.
But synthetic data is not automatically safe, not automatically compliant, and not automatically truthful. It has to be treated like a governed asset, with measured privacy risk, measured utility, and clear lineage. If you can’t prove those things, you don’t have synthetic data strategy, you have synthetic confidence.
If you’re mapping where synthetic data belongs in your GenAI roadmap, EM360Tech’s research and expert-led analysis can help you pressure-test the trade-offs before they turn into production surprises.
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