With the growing importance of artificial intelligence systems, especially those based on image and video analysis, privacy protection and regulatory compliance, including the GDPR, are becoming a key concern. Real-world data containing personal information can expose companies to the risk of breaches, loss of consent or control over data and additional legal obligations. In this context, synthetic data is becoming an attractive alternative – it can significantly enable AI development while simultaneously reducing the risk of privacy violations.
One company strongly promoting this approach is SKY ENGINE AI. It's worth examining whether and how synthetic data can support regulatory compliance and what advantages and limitations it presents in this context.
What is synthetic data-and how SKY ENGINE AI generates it?
Synthetic data is data created artificially-through simulations, 3D renderings, or algorithms-rather than directly derived from real people or events. In the context of computer vision, this means, for example, generating images, videos, 3D scenes, varying lighting, materials and sensors and then automatically generating "ground truth"-labels, semantic masks, depth maps, 3D points, etc.
The SKY ENGINE AI platform offers a Synthetic Data Cloud service that enables companies to generate such synthetic datasets at scale-with automatic labeling, support for multiple sensors/modalities, the ability to configure scenarios and control over parameters.
Through this approach, instead of collecting and anonymizing real data (which can be costly and risky), companies can create "clean" datasets that do not relate to specific individuals-which should significantly facilitate compliance with data protection regulations.
How synthetic data helps with GDPR Compliance and Privacy Protection?
Here are the key benefits that synthetic data - exemplified by the SKY ENGINE AI approach - brings to bear on privacy and regulatory concerns:
- No Personal Data – No Privacy Risk: because synthetic data does not originate from real individuals, it does not contain personally identifiable information (PII), eliminating the risk of personal data disclosure.
- Secure AI Testing and Development: AI models can be trained, tested and validated without the use of real data - this is important in areas such as medicine, automotive and industrial settings, where privacy and regulations are particularly stringent. SKY ENGINE AI provides tools for generating high-quality synthetic images and data for these applications.
- Easier Collaboration and Data Sharing: synthetic data can be shared internally or with partners/external providers without the risk of breaches - simplifying collaboration across teams or organizations, even across jurisdictions.
- Covering rare or extreme scenarios without risk to real people: in applications where rare, extreme, or difficult-to-capture scenarios are important - e.g., detecting anomalies, damage, or unusual conditions - synthetic data allows AI to be trained without compromising privacy.
This allows synthetic data - especially when provided by professional platforms like SKY ENGINE AI to become a solid foundation for the compliant and secure development of AI systems.
Why synthetic data is worth considering from a regulatory and ethical perspective - and how to do it right?
If your organization plans to develop AI systems in sensitive areas (medicine, surveillance, automotive, security), synthetic data is an option worth seriously considering. Here are a few principles that help you use it responsibly:
- Choose a trusted provider: using a platform like SKY ENGINE AI, you have access to generation tools, ground truth, multimodality and integration with ML pipelines.
- Design generation with privacy in mind: it's important that data is created in a way that minimizes-and ideally eliminates-the risk of referencing real people. The generation process, randomization, parameters and randomization must be documented.
- Conduct a risk assessment and privacy audit: verify that synthetic data does not allow for re-identification, avoid duplicating unique characteristics of individuals and test for vulnerabilities to attacks such as singling-out, linkability, etc. Academic research emphasizes that synthetic data-although often more secure than raw data-does not guarantee anonymity.
- Combine synthetic and real data where necessary: in many applications, a hybrid approach offers the best balance: synthetics provide scale, privacy and flexibility; real data adds authenticity, natural variability and context.
Synthetic data is the path to regulatory and responsible AI
In a world where data protection regulations are increasingly influencing the pace of AI development, synthetic data - if used properly - is becoming a viable tool for combining innovation with respect for privacy. Platforms like SKY ENGINE AI demonstrate that generating synthetic data can be professional, scalable and organized - offering resources that:
- do not contain personal data,
- can be widely shared and used in AI testing, development and production,
- minimize the risk of breaches, pseudonymization, or compliance issues,
- accelerate the development of AI projects where real data is difficult, expensive, or impossible to obtain.
At the same time - it's important to remember that synthetics are not a magic solution that guarantees security. They require proper design, auditing, testing and - in many cases-prudent integration with real data.
If your company is working on AI systems that may touch on privacy issues, consider synthetic data as a conscious and responsible choice. They enable AI to be built that is compliant, ethical and scalable - while maintaining user safety and regulatory compliance.