Securing AI in Global Health Research: A Framework for Cross-Border Data Collaboration

Artificial intelligence (AI) is transforming global health research by enabling advanced data analytics for disease modeling, clinical trials, and personalized medicine. However, cross-border data sharing introduces significant challenges related to security, ethics, and regulatory compliance, particularly concerning patient privacy, cybersecurity threats, and adherence to standards such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and emerging AI regulations. The increasing sophistication of cyber threats, coupled with inconsistent legal frameworks, underscores the urgent need for robust security measures. This paper explores AI-enhanced security frameworks designed to facilitate secure and ethical global data collaboration while preserving data integrity, patient confidentiality, and equitable access to healthcare advancements. We propose a novel security model that integrates federated learning, blockchain technology, and AI-driven threat detection to mitigate risks associated with cross-border health data exchange. These technologies enable decentralized data processing, enhance security through immutable ledgers, and proactively identify cybersecurity threats in real time. Our approach is particularly relevant to rare disease research, drug development, and pandemic preparedness, where seamless yet secure international data sharing is crucial for advancing medical science while safeguarding sensitive patient information.