AI Governance Framework for Health Data and Sensitive Domains: A Comprehensive Approach to Ethical Data Utilization (Published)
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.
Keywords: AI governance, context-aware access control, healthcare data protection, informed consent orchestration, sensitivity risk index
API-Driven Security and Compliance in Digital Health Infrastructure: Leveraging Middleware for Comprehensive Protection of Patient Data (Published)
This technical article demonstrates the critical intersection of API security, middleware architecture, and regulatory compliance within modern healthcare information systems. As healthcare organizations increasingly adopt cloud-based and API-driven infrastructures, they face unique challenges in protecting sensitive patient data while maintaining operational efficiency. This article presents a comprehensive framework for implementing secure API ecosystems that leverage token-based authentication, zero-trust principles, and centralized policy enforcement through middleware platforms. By exploring implementation patterns across hybrid environments, the research demonstrates how properly architected API security can simultaneously address regulatory requirements like HIPAA and GDPR while enabling innovation in healthcare delivery. The proposed approach integrates robust identity management, fine-grained access controls, and comprehensive audit logging to create a security posture that protects patient data throughout its lifecycle across distributed clinical systems.
Keywords: API middleware security, healthcare data protection, regulatory compliance automation, token-based authentication, zero trust architecture