Synthetic Data – Application in AI testing and development

Synthetic data (synthetic data) is artificially generated information that replicates the statistical and structural characteristics of real data, but does not contain real, identifiable information. They are becoming an important alternative when access to authentic data is limited by legal (such as RODO), ethical or logistical barriers.

Although the technology is growing rapidly, a realistic understanding of its advantages and limitations is key. The increase in interest is mainly driven by two factors. First, privacy regulations (e.g., RODO) make it difficult to process personal data, and synthetic data can help circumvent some of the restrictions – although it does not provide automatic exemption from legal requirements. Second, there is a need for a variety of data, especially for rare scenarios. Synthetic data makes it possible to generate them, but ensuring their fidelity and realism remains a challenge.

Among the potential benefits are reduced privacy risks, the ability to generate hard-to-collect test scenarios, and closing data gaps. However, promises of eliminating bias should be approached with caution. Generators often carry over and even amplify biases present in the source data. They reproduce statistical trends – if the input data contains problematic patterns, the synthetic data will likely replicate them. The main real challenges are the difficulty of faithfully reproducing complex patterns, the aforementioned risk of replicating biases, and the complexity of validating the quality of the generated data. Effective use of this technology requires a deep awareness of its capabilities and limitations.

The process of generating synthetic data is based on advanced statistical models and machine learning techniques. Essentially, it involves building a model that learns distributions and relationships in real data, and then uses this knowledge to generate new artificial samples.

Implementation usually begins with an in-depth analysis of the source data – identification of variables, their distributions, correlations and constraints. This is a key step, determining the quality of the result. Then a suitable generative algorithm is selected and trained. Popular ones include:

• Generative Adversarial Networks (GANs): Two competing networks produce realistic data (especially images), but their training is sometimes unstable.
• Variational Autoencoders (VAEs): Offer more stable training and better control, sometimes at the expense of less detailed data.
• Diffusion Models: Achieve high quality (especially images), but require huge computational resources.
• Statistical methods (e.g., copula-based): Effective for tabular data, preserve correlations well, less computationally demanding, but more difficult for non-standard distributions.

An important, often overlooked technical challenge is maintaining relational data structures. While generating a single table is relatively easy, faithfully mapping complex relationships between tables (e.g., in databases) with consistency is much more difficult. Dedicated frameworks (like Synthetic Data Vault) try to cope with this, but their effectiveness depends on the specific case.

What advantages and limitations does synthetic data offer over real data?

Synthetic data has potential advantages, but also significant limitations. The main advantage is flexibility – the ability to generate large volumes and specific scenarios (such as rare cases). The price for this is the risk that the generated data will not reflect the subtleties and “dirt” of the real world, which can lead to models that fail in production (the so-called “synthetic gap”).

The privacy aspect is sometimes oversimplified. Synthetic data generally reduces risk, but does not eliminate it completely. Advanced attacks (e.g., membership inference) can reveal information about the source data under certain conditions. Similarly, quality control is complex. Some problems can be eliminated, but the generation process can introduce new errors that are difficult to detect, such as subtle statistical biases. Models trained on overly “clean” data may be less robust.

The table below summarizes the key differences more succinctly:

How does synthetic data affect privacy and RODO compliance?

Synthetic data is often seen as a solution to RODO problems, but the situation is more complex. The key question – whether they fall under RODO – has no clear answer. It depends on the method of generation and the risk of re-identification (the ability to reproduce information about specific individuals). If such a risk exists, synthetic data may still be considered personal data.

Organizations must be able to prove and document that the risk of re-identification is negligible, which often requires a formal assessment (e.g., DPIA). It is more realistic to view synthetic data as a means of minimizing risk, rather than eliminating it. Properly implemented, they can lower the level of data sensitivity, potentially allowing for less stringent security measures. Simplifications in compliance are possible, but rarely mean a complete waiver.

A clear benefit is international data transfers, where the exchange of generators or synthetic data can replace complex legal procedures for personal data.

Bottom line: synthetic data reduces (but does not eliminate) privacy risks, can reduce procedural burdens (with evidence of low risk), and facilitates international transfers. However, they require formal risk assessments, documentation of techniques, legal consultation, and consideration of vulnerability tests for information disclosure attacks.

How does synthetic data affect the effectiveness of testing AI systems?

Synthetic data can significantly improve AI testing, but they also introduce new challenges. Their main advantage is the ability to systematically generate test scenarios that are missing from real data – such as rare edge cases, data for attack resilience testing, or simulations for performance testing. This allows for more comprehensive coverage and building more resilient systems.

However, effectiveness critically depends on the quality and realism of the data generated. Testing on unrealistic data can lead to false conclusions. Therefore, rigorous validation of the synthetic data itself is essential. It is also important to keep in mind that synthetic data may have different characteristics than real data (e.g., less “dirt”), which affects results, especially performance tests.

In practice, a hybrid approach is most effective: using synthetic data for early problem detection and broad coverage, followed by validation and fine-tuning on real data. In the context of MLOps, it is crucial to monitor the so-called “synthetic gap” – the difference in model performance on the two types of data.

What methods of generating synthetic data are most effective in 2024?

Evaluating the effectiveness of generation methods depends on the context: use case, data type and resources. There is no single “best” method. Diffusion models target visual data quality, but are very resource-intensive. GANs offer a good compromise of quality and performance for images, but are sometimes unstable. VAEs are more stable and good for structured data, but less detailed. For tabular data, statistical methods (e.g., copulas) are often sufficient, capturing correlations well and easier to interpret. Textual data is mainly generated using linguistic models (Transformers).

Organizations often use a hybrid or tailored approach. It is important to remember that method alone is not enough – a rigorous validation process for the generated data is also key. The table below succinctly summarizes the main techniques:

Can synthetic data completely replace real-world data in AI training?

This is a controversial question. Currently, the answer is: in most cases not yet, and in some cases probably never. It is argued that the subtleties and “noise” of real data are fundamental to building resilient models. While advances in synthetic data quality have been impressive, especially where real data are extremely sparse, some limitations remain.

The ability to substitute real data depends on the domain and risk (in critical applications substitution is unlikely), the phase of model development (synthetic more useful in early phases) and the nature of the task (perceptual models are more sensitive).

Studies have consistently shown the existence of a “synthetic-to-real gap” – a difference in the performance of models on synthetic versus real data. Therefore, currently the most pragmatic approach is a hybrid strategy: initial training on synthetic data, followed by tuning and validation on real data (“synthetic-to-real transfer learning”). This significantly reduces the need for real data while maintaining high performance.

What technical challenges accompany the implementation of synthetic data in IT projects?

Implementing synthetic data poses a number of practical technical challenges. A key one is ensuring quality and statistical fidelity, which requires rigorous validation beyond basic metrics. Equally important is seamless integration with existing data pipelines and CI/CD processes, which is often complex and requires standardization (e.g., containerization, APIs).

“Concept drift” must also be managed, regularly updating generators as real-world data evolves. Generation efficiency and scalability can be a challenge, especially with advanced methods. Effective management of metadata and data provenance (lineage) for transparency and auditing is essential. There is also often a skills gap – the need for expertise in various fields.

The following table succinctly summarizes these challenges:

How does synthetic data affect the development of AI in sensitive sectors (e.g., medicine, finance)?

In sectors with high regulatory and ethical requirements, such as medicine or finance, synthetic data offers opportunities, but its implementation faces specific challenges. In medicine, clinical reliability is key, and synthetic data must accurately capture subtle pathological patterns. While results are promising, there is often a performance gap, so hybrid or federated approaches are preferred.

In finance, synthetic data help test fraud detection and risk modeling, but have trouble mapping unprecedented crisis events (“black swans”). The solution is to supplement them with expert-designed scenarios.

In both sectors, the position of regulators (e.g., FDA, EBA) is key, treating synthetic data mainly as a complementary tool, requiring rigorous validation, especially for critical applications.

How to practically measure the quality and reliability of the generated synthetic data?

Effective assessment of synthetic data quality requires a multidimensional approach. At least three aspects should be evaluated:

1. Statistical Fidelity (Fidelity): How well does synthetic data reproduce the statistics of real data? Analysis of univariate and multivariate distributions, comparison of correlations (not just basic statistics).
2. Practical Utility (Utility): Is the data fit for purpose? Comparison of performance of models trained on synthetic vs. real data (TSTR), tests for specific scenarios, evaluation of realism by domain experts.
3. Privacy (Privacy): What is the risk of disclosure of information? Testing for resilience to attacks (e.g., inferring affiliation), assessing distance to nearest neighbors, possibly using techniques with formal guarantees (e.g., differential privacy).

In practice, it is worthwhile to use cross-validation, visualize comparisons and involve different stakeholders (ML engineers, domain experts, security specialists) in the evaluation process.

What industries are already using synthetic data in testing AI solutions?

Synthetic data is being actively implemented in several industries, albeit with varying success. The automotive sector uses simulations to test autonomous systems, but in a hybrid model. The financial sector uses them to test fraud detection and risk modeling, often supplementing with expert scenarios. In health care, they help supplement data for rare diseases, but mainly play a supporting role. Other industries, such as retail, industry and cybersecurity, are also experimenting, facing specific challenges (e.g., difficulties in modeling complex behavior or realistic attacks). The table below succinctly summarizes the situation:

What trends in synthetic data will shape the future of artificial intelligence by 2030?

The future of synthetic data seems promising, but development is likely to be evolutionary. Key trends through 2030 include: advances in generating consistent multimodal data, a two-pronged development of tools(democratization of low-code vs. specialization for experts), attempts to integrate causal inference (although this is difficult), growing demand for validation and certification standards, deeper integration with MLOps, and development of techniques that provide privacy with measurable guarantees.

Realistically, by 2030, we can expect significant advances in multimodal data, specialized industry generators and validation standards. Causality modeling, computational challenges, regulatory uncertainty and the “synthetic gap” problem for critical applications may remain barriers.

How to practically integrate synthetic data into the company’s existing data pipelines?

Integrating synthetic data with existing infrastructure requires a thoughtful approach. Integration points (source, intermediate, end) should be defined, preferring an incremental approach. It is crucial to manage metadata and provenance (lineage) to unambiguously tag synthetic data and track its parameters. Lifecycle automation (drift monitoring, re-training, generation, validation) within CI/CD processes is essential. Generators should be treated as ML artifacts (versioning, tracking).

Experience shows that success depends on clear data labeling, incremental approaches, automation and team collaboration. Typical pitfalls include underestimating the complexity of integration, lack of procedures for problems, skipping training and overly ambitious automation at the start.

Does synthetic data actually reduce AI development costs – a realistic perspective

The promise of significant cost reductions from synthetic data is often exaggerated. The potential savings in data acquisition, labeling and compliance are real, but must be confronted with new costs: infrastructure (GPU, licenses), expertise, validation processes (a new task) and system maintenance. In addition, the lower quality of the model resulting from the “synthetic gap” may generate hidden costs.

Analyses indicate real savings of 15-30% of total data costs, which is significant but far from the marketing promises. A more tangible benefit may be time-to-market acceleration, although initial implementation takes time. The table below summarizes a realistic cost perspective:

Conclusions: Synthetic data changes the cost structure, and its value often lies more in flexibility and risk reduction than in direct financial savings.

Tools and frameworks for practical applications – strengths and weaknesses

The choice of tool is key. Commercial (Enterprise) platforms (e.g. MOSTLY AI, Gretel) offer ease of use and support, but are expensive and less flexible. Open-source libraries (e.g., SDV, TensorFlow Privacy) offer full control and no licensing costs, but require a high degree of technical expertise and self-assurance of quality and privacy. Specialized domain generators provide high quality for specific applications, but at the expense of versatility. There are also support tools for validation or integration with MLOps. The choice depends on the needs, scale, budget and competence of the team. The table below synthesizes these options:

Complex ethical implications of using synthetic data in AI systems

The ethics of synthetic data go beyond privacy. Key challenges include the risk of bias propagation and amplification (bias amplification), as generators can intensify inequalities from training data. Also problematic is the blurring of responsibility (accountability gap) – the difficulty of assigning blame for model errors. Attention should be paid to inequalities in access to technology, which can exacerbate the digital divide.

There are also questions about the transparency and explanatory power of models trained on synthetic data, and the potential for abuse (e.g., deepfakes). Authenticity and representation are also an issue, especially when generating data on minority groups.

Responsible use requires ongoing ethical reflection, a holistic approach that combines technical solutions (e.g., fairness audits) with transparent processes and consideration of all stakeholders’ perspectives.

How synthetic data supports AI development in a data-constrained environment – real opportunities and limitations

Synthetic data can help overcome the problem of limited access to data, such as supplementing small collections in niche fields, facilitating international cooperation (exchanging generators instead of data), or enabling prototyping.

However, their effectiveness is strongly dependent on the quality of the input data – the generator will not create knowledge from nothing. There is a risk of over-fitting to a small sample and lossy compression of information. Validation is more difficult in the absence of real data. Experience from emergencies has shown the limited effectiveness of early models based only on synthetic data.

The table below summarizes the effectiveness in different scenarios:

Conclusions: Synthetic data is a valuable complementary tool, but not a miracle solution to a lack of data. A hybrid strategy seems the most pragmatic.

Summary: A Realistic Look at Synthetic Data in AI.

Synthetic data is an important and rapidly growing area in AI, offering solutions to problems of data availability, privacy and cost. However, they require a balanced and critical approach.

Technically, the methods are mature, but challenges (fidelity, privacy, integration) remain. The hybrid approach is currently the most pragmatic. Business-wise, the benefits lie more in flexibility and risk reduction than in drastic cost cutting, and implementation requires a strategic approach and consideration of TCO. Ethically, new dilemmas are emerging (bias, accountability, transparency), requiring systematic management.

Looking ahead, we can expect progress, but development will be shaped by social, regulatory and economic factors. Organizations should take a pragmatic approach: start small, invest in competencies, implement rigorous validation and systematically evaluate all aspects. The key is realism – appreciating potential, but being aware of limitations.

Synthetic Data – Key Lessons for Practitioners

• Business benefits are often different from promises – sound analysis is required.
• It is a powerful complementary tool, not a panacea.
• Context matters – effectiveness depends on the domain and use case.
• It is necessary to balance technology with ethics.
• The hybrid approach (combining with real data) is usually the best.
• Implementation requires consideration of the entire ecosystem (people, processes, technology).