AI
Test data management
AI
Synthetic data generation
Can AI-generated test data be high-quality, safe and compliant?
Bert Nienhuis
Can AI-Generated Test Data Be Safe and Compliant? | DATPROF
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In my most recent post I evaluated AI-generated synthetic test data. This is a follow up post where I want to dive deeper into the appeal and its limitations. For those who are new to the topic, synthetically generated test data created by AI models is a ‘recent’ development in the software testing industry. Here’s a brief explanation.
AI-generated syntehtic test data is generated by AI models that aim to provide an alternative to using production data in testing environments. Generally, there are two ways AI models generate test data:
- Training an AI model on production data
- Using generative AI to create data that mimics production data
While synthetic test data may sound like the ideal solution—realistic, quick, and cost-effective—it’s far from perfect. The reality is that generating high-quality test data with AI is often complex, time-consuming, and expensive. Moreover, the process involves significant challenges related to transparency, reliability, and compliance with privacy regulations like GDPR and CCPA.
Let’s look why AI-generated test data, at the moment, often falls short of being a truly viable solution.
The appeal of AI models – and their limitations
AI models can indeed produce test data that resembles production data, especially when trained on actual production datasets. But there’s a catch. The best results come from feeding the AI model with real, production data—a direct conflict with privacy laws and data protection standards.
Why this is problematic:
- Transparency and reliability issues: these complex AI systems are often “black boxes,” meaning we don’t fully understand how they produce the data. Without this clarity, it’s hard to guarantee quality and consistency.
- Legal noncompliance: training AI models on production data violates core principles of privacy legislation. Regulations like GDPR and CCPA demand explicit consent and restrict the use of personal data for secondary purposes.
This fundamental conflict is where the promise of AI-generated test data begins to unravel.
Why AI often falls short in compliance
Let’s address the big question: Can AI-generated test data be compliant?
There are two answers to this question. The first answer is:’ it could’. If a generative AI model produces data that closely resembles production data without ever using actual production data or data containing personal information, it would appear to be compliant under the current legal framework.
But if it is an AI model that is trained on production data containing personal information, the answer is a resounding no. Privacy laws like GDPR and CCPA mandate consent, transparency, and strict limitations on the use of personal data (1,2). Using anonymized production data might seem like a workaround, but isn’t it an added complexity?:
If the data is already anonymized, why not use it directly for testing instead of adding another layer of complexity by generating synthetic data?
So what do we end up with? A method that is most often non-compliant but also less efficient and more labor-intensive than necessary. Even when AI-generated test data avoids production data altogether, such as through user-defined rules or generative AI, significant challenges remain:
- Lack of scalability: Generative AI struggles to produce consistent, high-quality test data across complex systems.
- Opaque processes: Without understanding how the data is created, it’s impossible to fully trust its accuracy or reliability.
Are these methods solving the problem – or creating new ones?
The goal of synthetic test data is admirable: creating compliant, high-quality data without relying on production data. But the methods we have today fall short:
- Training AI models on production data directly conflicts with privacy regulations.
- Generative AI lacks the scalability and transparency needed for reliable test data generation.
This raises a critical question:’aren’t these methods worse than the problem we are trying to solve?’
In summary
AI-generated test data may be a step in the right direction, but it’s not the fast, easy, or compliant solution it’s often portrayed to be. Whether through training on production data or using generative AI, the current methods fail to deliver on scalability, compliance, and simplicity.
At DATPROF, we believe in exploring innovative solutions while staying firmly rooted in compliance and practicality. Want to dive deeper into the complexities of generative AI for test data? Check out our detailed article on the topic.
Frequently Asked Questions
1. Can AI-generated test data be high-quality, safe and compliant?
AI-generated test data can be useful, but it is not (automatically) high-quality, safe or compliant. Teams need to validate how the data was generated, whether production data was used, whether the output is representative, and whether the process meets privacy requirements such as GDPR or CCPA.
2. Why is AI-generated test data difficult to trust?
AI-generated test data can be difficult to trust because AI models often work as black boxes. If teams cannot fully explain how the data was created, it becomes harder to prove quality, consistency, reliability and compliance.
3. Is training AI models on production data compliant?
Training AI models on production data that contains personal or sensitive information is generally risky and may conflict with privacy principles around consent, purpose limitation and transparency. If production data is already anonymized, teams should also question whether generating synthetic data on top of it adds unnecessary complexity
4. What are the main limitations of AI-generated synthetic test data?
The main limitations are lack of transparency, limited scalability, unreliable consistency across complex systems and uncertainty around compliance. These issues become more serious when test data needs to reflect many tables, dependencies, business rules and edge cases.
5. Can generative AI create test data without using production data?
Yes, generative AI can create test data without directly using production data. However, the generated data still needs to be validated for realism, structure, business rules, relationships and edge cases. Otherwise, it may look plausible but fail to support meaningful testing. Therefor it is highly unrecommended.
6. When is AI-generated test data useful?
AI-generated test data can be useful for small datasets, examples, early-stage scenarios or supplementing existing test data. It is less suitable as a standalone solution for large, regulated enterprise environments where repeatability, explainability and consistency are essential.
7. How does DATPROF approach AI-generated test data?
DATPROF approaches AI-generated test data with caution and practicality. The goal is not to use AI because it is new, but to solve the test data problem with the right technique, such as anonymization, synthetic data generation, subsetting, automation or a hybrid approach.
Sources
- Regulation – 2016/679 – EN – gdpr – EUR-Lex. (z.d.-b). https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng?utm_source=chatgpt.com
- Synthetic data. (2025c, januari 28). European Data Protection Supervisor. https://www.edps.europa.eu/press-publications/publications/techsonar/synthetic-data_en