AI Governance Framework for Health Data and Sensitive Domains: A Comprehensive Approach to Ethical Data Utilization

The integration of artificial intelligence in healthcare presents transformative opportunities while introducing complex governance challenges. This article introduces a novel domain-specific AI governance framework designed for health and biometric data, addressing the intricate interplay between innovation, privacy, regulatory compliance, and ethics. The model employs a dynamic, adaptable structure across strategic, tactical, and operational levels to evolve alongside technological advancements and regulatory shifts. At its foundation lie three essential pillars: informed consent orchestration, which reimagines consent as an ongoing process; context-aware data access, extending beyond traditional role-based controls; and dynamic risk assessment, providing continuous evaluation of ethical and legal implications. Central to this framework is the Sensitivity Risk Index, offering standardized metrics for evaluating risk across identifiability potential, intrinsic sensitivity, harm potential, and consent alignment dimensions. Healthcare organizations implementing similar governance approaches have demonstrated marked improvements in regulatory compliance, patient trust, operational efficiency, and innovation capacity. By integrating legal requirements with technical enforceability, this framework provides practical pathways to balance innovation with protection, offering guidance for healthcare organizations, technology developers, and regulatory bodies seeking to harness AI benefits while maintaining the highest standards of data protection and ethical practice.