AI-Integrated Cybersecurity Framework for Medical Data Protection

Abstract

As the world becomes increasingly digitalized, especially in healthcare systems, sensitive medical information is a concern that must be protected. Conventional security measures tend to miss out on sophisticated cyber attacks, particularly in settings handling confidential patient data. This paper suggests an AI-based cybersecurity framework that provides real time security for medical data through intelligent surveillance and adaptive reaction. The system employs machine learning algorithms to identify anomalous access patterns, reinforce authentication, and block unauthorized breaches. In contrast to current solutions that are dependent on IoT devices, this solution operates independently, minimizing hardware vulnerabilities and easing deployment. Role-Based Access Control (RBAC) is also enhanced with AI for dynamically adapting user permissions according to behaviour and risk analysis. The system is deployed with a light-weight Flask-based web API, providing security as well as scalability. Experimental findings verify that the suggested method enhances detection accuracy and system responsiveness while ensuring data confidentiality, integrity, and availability in medical environments.