CyVHealth: Intelligent cybersecurity architecture for secure virtual medical consultation

• The study proposes an AI-based cybersecurity system for virtual health platforms. • It ensures real-time threat detection and data protection using advanced AI. • Tested on 25 synthetic cases, the system showed strong security and compliance. This graphical abstract outlines the cybersecurity challenges in digital mental health platforms, especially during virtual therapy sessions. It proposes an AI-enabled system for effective threat detection and anomaly monitoring, ensuring secure and confidential virtual care 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.

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Publication Details

Journal
Cyber Security and Applications
Published
2025-08-22
DOI
https://doi.org/10.1016/j.csa.2025.100112
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
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article

CyVHealth: Intelligent cybersecurity architecture for secure virtual medical consultation

A. Jayanthiladevi, R Manasa
Cyber Security and Applications
Scientific Computing and Data Management
article

CyVHealth: Intelligent cybersecurity architecture for secure virtual medical consultation

A. Jayanthiladevi, R Manasa
article en

Abstract

• The study proposes an AI-based cybersecurity system for virtual health platforms. • It ensures real-time threat detection and data protection using advanced AI. • Tested on 25 synthetic cases, the system showed strong security and compliance. This graphical abstract outlines the cybersecurity challenges in digital mental health platforms, especially during virtual therapy sessions. It proposes an AI-enabled system for effective threat detection and anomaly monitoring, ensuring secure and confidential virtual care 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.

Cyber Security and ApplicationsVol. 3
Marwadi University (IN)
Openalex Percentile: Top 79%
Scientific Computing and Data Management
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CyVHealth: Intelligent cybersecurity architecture for secure virtual medical consultation — A. Jayanthiladevi, R Manasa · Cyber Security and Applications (2025) | TGRS Research Map | TGRS