CyVHealth: Intelligent cybersecurity architecture for secure virtual medical consultation
Year: 2025
Type: article
Abstract: With the growth of virtual treatment platforms and digital mental health services, it is now more important than ever to protect sensitive user data and maintain confidentiality. However, these networks have become increasingly vulnerable to online threats such as unauthorised access, data theft, and privacy invasion. This paper introduces an architecture for digitally delivered mental health services that enhances cybersecurity through the use of artificial intelligence (AI). The use of advanced artificial intelligence algorithms to identify, stop, and respond to security threats in real-time makes virtual therapy platforms protecting patient data with our methodology a stronghold. The growing reliance on virtual health platforms for managing chronic conditions like diabetes and thyroid disorders necessitates robust cybersecurity to protect sensitive health data, especially in mental health-related therapy sessions. This report evaluates a multi-layer AI-enhanced cybersecurity architecture in a simulated environment involving 25 synthetic patient samples undergoing virtual consultations. The goal is to analyse how AI models not only secure the sessions but also ensure data integrity, monitor health outcomes, and maintain regulatory compliance. Statistical analysis of session data, anomaly detection scores, and cyber incident response metrics reveals the feasibility and efficiency of AI-driven models in safeguarding virtual consultations.
Source: Cyber Security and Applications
Authors: Manasa R, Jayanthiladevi A
Institution: Marwadi University
Language: English
Topic: Internet of Things and AI
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413434191