Are Synthetic Data and Privacy Protection the Future of Artificial Intelligence Development? - Istiarsyah Istiarsyah; Tina Isnaeni; Rival Pahrijal (Westscience Press, 2026)
The rapid advancement of Artificial Intelligence (AI) has created increasing dependence on large-scale datasets, while simultaneously generating significant legal challenges related to privacy protection, data governance, and individual rights. This study examines whether synthetic data and privacy protection mechanisms can become the future foundation of responsible AI development through a normative legal analysis approach. The research analyzes relevant legal frameworks, regulatory principles, and conceptual developments concerning personal data protection, AI governance, and the utilization of synthetic data as a privacy-preserving alternative. The findings indicate that synthetic data provides substantial potential to reduce privacy risks by minimizing direct exposure to identifiable personal information while improving data accessibility for AI training and innovation. However, synthetic data does not automatically eliminate legal concerns, particularly regarding re-identification risks, accountability allocation, transparency, and regulatory uncertainty. The analysis demonstrates that effective AI governance requires a shift from traditional data protection approaches toward adaptive frameworks based on risk assessment, privacy-by-design principles, and responsible technology development. The study argues that synthetic data should not be viewed as a complete replacement for real-world data but as a complementary mechanism within a broader privacy-preserving AI ecosystem. Therefore, the future of artificial intelligence development depends on the integration of technological innovation and legally enforceable privacy protection frameworks that ensure transparency, accountability, and respect for fundamental rights.