Introduction

As the European Union (EU) advances its Artificial Intelligence Act (AI Act), organizations across Europe must adapt to a new era of regulatory compliance. The AI Act introduces a risk-based framework that categorizes AI systems into four levels (Unacceptable, High, Limited, and Minimal Risk), with stringent requirements imposed on high-risk AI applications.

While the AI Act has been formally adopted, and the first five articles took effect on February 1, 2025, companies have until 2026 to proactively align their AI solutions with compliance requirements before enforcement begins. This transition period provides organizations with a critical window to refine their AI models, enhance transparency, and integrate responsible AI practices.

In this evolving regulatory landscape, synthetic data is emerging as a crucial enabler of compliance, particularly for organizations working with high-risk AI applications. In this blog post, we will explore some of the ways synthetic data can help companies meet the AI Act’s requirements while ensuring their AI systems remain ethical, safe, and reliable.

The AI Act and Its Impact on AI Development

The AI Act is designed to ensure transparency, fairness, and accountability in AI development while balancing innovation with ethical considerations.

AI systems classified as high-risk, such as those used in credit scoring, healthcare diagnostics, recruitment, and law enforcement, must meet rigorous requirements in several key areas, such as:

• Data Governance: Ensuring high-quality, unbiased, and representative datasets.
• Privacy Protection: Complying with strict data protection regulations, including GDPR.
• Bias Mitigation: Preventing discriminatory outcomes by ensuring fairness in AI predictions.
• Technical Robustness: Enhancing model accuracy and resilience to adversarial attacks.

Meeting these requirements can be challenging, especially when working with sensitive or imbalanced real-world datasets. Synthetic data can offer a privacy-preserving and bias-mitigating approach to AI development.

The Role of Synthetic Data in AI Act Compliance

Synthetic data, that is artificially generated data that maintains the statistical properties of real-world datasets without containing actual personal information, has been recognized within the AI Act as a viable solution for bias mitigation, privacy protection, and technical robustness.

Where the AI Act Mentions Synthetic Data

Synthetic data is explicitly referenced in several articles of the AI Act, including:

• Article 10(5)(a): Allows the use of synthetic data when real-world data is insufficient for bias detection and correction.
• Article 50(2): Requires AI systems generating synthetic audio, video, text, or images to label outputs as artificially generated.
• Article 59(1)(b): Recognizes synthetic data as a compliant alternative when anonymized or other non-personal data is insufficient to meet regulatory requirements.

These provisions underscore the increasing regulatory acceptance of synthetic data as a tool to address AI compliance challenges.

Key Advantages of Synthetic Data Under the AI Act

Enhancing Data Governance (Article 10)

• Synthetic data can improve the quality and representativeness of training datasets.
• It enables organizations to fill gaps in real-world data without compromising regulatory requirements.

Ensuring Privacy and Data Protection (Article 10)

• Unlike real data, synthetic datasets do not contain personally identifiable information (PII), reducing the risk of privacy violations.
• This helps companies comply with both the AI Act and GDPR, eliminating the need for complex data consent management.

Mitigating Bias and Promoting Fairness (Article 10)

• AI systems trained on biased real-world data can reinforce societal inequalities.
• Synthetic data can be generated to balance demographic representation and ensure fair AI decision-making.

Enhancing Model Accuracy and Robustness (Article 15)

• Synthetic data allows AI models to train on diverse scenarios, improving performance under different conditions.
• It strengthens AI resilience by simulating rare or edge-case scenarios that may be missing from real datasets.

Real-World Applications of Synthetic Data in High-Risk AI Systems

The benefits of synthetic data are particularly evident, for instance, in two high-risk AI domains identified under the AI Act:

1. Financial Sector: Credit Scoring

• Challenge: Traditional credit scoring models often rely on historical financial data, which can introduce bias against underrepresented groups.
• Solution: Synthetic financial data can be generated to improve representation, ensuring that AI models do not unfairly disadvantage specific populations.
• Outcome: More inclusive and fairer lending decisions while remaining compliant with the AI Act’s bias mitigation requirements.

2. Healthcare Sector: AI-Driven Patient Diagnostics

• Challenge: AI-based healthcare models require large volumes of patient data, which raises privacy concerns under GDPR and HIPAA regulations.
• Solution: Synthetic patient records allow AI models to be trained on diverse medical conditions without exposing sensitive health information.
• Outcome: Accelerated AI-driven medical advancements without compromising patient privacy.

Future Implications and Conclusion

As AI governance frameworks continue to evolve, synthetic data will play an increasingly strategic role in responsible AI development. Organizations that leverage synthetic data effectively will not only ensure compliance with the AI Act but also gain a competitive advantage by fostering innovation within a legally sound framework.

As discussed earlier, companies have until 2026 to align their AI systems with the AI Act’s compliance requirements. This transition period presents an important opportunity for businesses to adopt synthetic data as an enabler of compliance, allowing them to refine their models, enhance fairness, and ensure privacy protection before enforcement begins.

So, synthetic data has emerged as a critical tool in this new landscape, helping organizations meet privacy, bias mitigation, and technical robustness requirements while maintaining innovation.

By embracing synthetic data, businesses can proactively navigate compliance challenges and contribute to the EU’s vision of a trustworthy, human-centric AI ecosystem.