Paper Details

Name: Dipak Vijay Rajput
Country: India

Year: 2026
Volume: Volume-13, Issue-1 (January-June)
Page Number: 339-351
DOI: https://doi.org/10.5281/zenodo.20522018

The fast growth of digital tools and those pretty tightly connected social networks has, in practice, pushed cybersecurity challenges upward across areas like healthcare , banking, education, governance, and communication. A lot of older supervision strategies in cybersecurity tend to rely on rule driven and signature based safeguards, yet they often don’t handle todays threats well—like phishing, ransomware, malware, and even AI driven breakins, where the patterns kind of shift. Because of that, this work puts forward a Hybrid Sensitivity Supervision Model for Cybersecurity in Social Systems, that sort of blends Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics, cloud hardening, IoT monitoring, and regulatory compliance into one cybersecurity framework. The research route is descriptive, analytical, and also empirical. It uses surveys, case studies, and a comparative performance evaluation ,with a sample of 100 people respondents, though that part is a bit limiting but still gives useful signals. To check how effective the whole setup is, regression analysis , ANOVA, and correlation techniques are applied. In general, the outcomes show that the proposed hybrid model increases threat identification precision, shortens the response cycle, improves scalability, and supports real time monitoring more effectively than more traditional cybersecurity systems. So the study adds value both in theory and in practice, because it offers an adaptive, expandable, and intelligent supervision structure that fits modern digital social contexts.