**Main Idea: Novel Access Control Model Using Language AI and Ontology****GPT-Onto-CAABAC Framework*** Integrates Generative Pre-trained Transformer (GPT) models, ontology, and Context-Aware Attribute-Based Access Control (CAABAC)* Enhances access control decisions by interpreting healthcare regulations, adapting to real-time context, and considering user attributes**Benefits:*** **Adaptable:** Contextual analysis and ontology mapping provide flexibility in changing environments.* **Context-Aware:** CAABAC dynamically assesses real-time attributes and policy compliance.* **Attribute-Based:** Granular control of access based on specific attributes relevant to healthcare.* **Scalable:** Efficient and robust to handle large-scale access requests.**Evaluation:*** Positive results in scenario testing and evaluation metrics.* Potentially high accuracy and response time, addressing limitations of traditional models.**Beyond Healthcare:*** Applicability in other sectors with similar access control requirements (e.g., finance, legal).* Potential for expanded use cases beyond EHR (e.g., research, public sector).**Challenges and Future Directions:*** Interpretability: Ensuring comprehension of AI-driven decisions for stakeholders.* Scalability: Optimizing performance for large-scale implementations.* Resource Requirements: Balancing performance with resource consumption.* Data Privacy: Maintaining security and privacy during data processing.* Continuous Learning: Adapting to evolving healthcare regulations and AI technologies.**Conclusion:*** GPT-Onto-CAABAC is an innovative access control framework with significant potential to improve healthcare data security and address industry challenges.* Future research can focus on enhancing scalability, interpretability, and ensuring ethical implementation.* The framework can revolutionize access control auditing not only in healthcare but also across other sectors.