Adaptive Access Governance in Healthcare

Area of Science:

• Health Informatics
• Cybersecurity
• Information Governance

Background:

• Insider threats are the primary cause of healthcare data breaches.
• Existing role-based access control (RBAC) systems struggle to adapt to dynamic clinical environments.
• Need for more responsive and granular access control mechanisms in healthcare.

Purpose of the Study:

• To present an adaptive access governance model for healthcare.
• To integrate RBAC with attribute-based and risk-based authorization.
• To incorporate continuous behavioral anomaly detection for enhanced security.

Main Methods:

• Developed an adaptive model integrating RBAC, attribute-based, and risk-based authorization.
• Incorporated continuous behavioral anomaly detection.
• Utilized contextual factors (time, device, location, sensitivity) for real-time access decisions.
• Implemented dynamic risk scoring.

Main Results:

• Demonstrated reduced instances of inappropriate access in simulated and operational settings.
• Validated the model's effectiveness in enhancing data security.
• Showcased improved alignment with regulatory standards like GDPR, HIPAA, and ISO/IEC 27001.

Conclusions:

• The proposed adaptive access governance model effectively mitigates insider-originated data breaches in healthcare.
• Integration of multiple access control strategies and behavioral analysis provides robust security.
• The model supports compliance with major data protection regulations.