Analyzing Cultural Vulnerabilities and Cyber Behavior through AI Enabled Security Frameworks
Srinivas Cheekati, Naveen Kannam, Chandrakanth Reddy Borra, Vandana Roy
Abstract
Cultural sensitivities and cyber habits are vital elements that determine the security situation, especially in the multicultural cyber world. The present work proposes a security framework enabled with AI that aims at analysing and mitigating cultural and behavioural risk during cyber activities. The framework is enabled by machine learning algorithms such as deep learning classifiers and context-aware anomaly detection models, to identify not-so-obvious cultural biases, behavioural anomalies, and cognitive decision-making weaknesses which are causes of cyber threats. Multi language data are run through natural language processing and in distributed demographics, federated learning is used to ensure that data privacy is ensured across the various demographics. The intended system is dynamic in correlation of behavioural metadata to regional cybersecurity threats to enable prior planning on mitigation of such threats. It includes risk scoring practices and culture profiling that allows it to adjust its own response through localized trends on cyber behaviours. Present safeguards are greatly enhanced by testing results that show the framework is effective in precisely identifying problems based on culture. The method has revolutionised the domains of cultural diversity and internet crime mitigation. With a score of 94.8% on the Cultural Sensibility Index in the dynamic scenario, the suggested method generated the highest levels of accuracy indications.