  As digital healthcare evolves, the security of electronic health records
(EHR) becomes increasingly crucial. This study presents the GPT-Onto-CAABAC
framework, integrating Generative Pretrained Transformer (GPT), medical-legal
ontologies and Context-Aware Attribute-Based Access Control (CAABAC) to enhance
EHR access security. Unlike traditional models, GPT-Onto-CAABAC dynamically
interprets policies and adapts to changing healthcare and legal environments,
offering customized access control solutions. Through empirical evaluation,
this framework is shown to be effective in improving EHR security by accurately
aligning access decisions with complex regulatory and situational requirements.
The findings suggest its broader applicability in sectors where access control
must meet stringent compliance and adaptability standards.
