AI-Augmented Cybersecurity Frameworks for Rural Educational Ecosystems in Nigeria: A Context-Aware Zero Trust Approach

Abstract

The increasing use of digital learning in Nigeria has exposed schools to cybersecurity threats, including phishing, malware, ransomware, unauthorised access, cyberbullying, and data breaches. Rural schools are particularly vulnerable because of unreliable connectivity and electricity, limited infrastructure, shortages of technical expertise, low digital literacy, and financial constraints. This paper proposes an artificial intelligence (AI)-augmented cybersecurity framework, grounded in Zero Trust Architecture (ZTA), for rural educational ecosystems in Nigeria. The framework integrates a lightweight Isolation Forest anomaly-detection model with continuous identity verification, role-based access control, context-aware policy enforcement, offline resilience, and gamified cybersecurity education. Security telemetry from authentication, device, network, and application logs is processed locally or through lightweight services to generate behavioural risk indicators. These indicators inform, but do not override, mandatory authentication and authorisation decisions. The framework is designed for low-resource environments and is suitable for use on mobile devices, cloud-based learning platforms, school-owned systems, and in a BYOD environment. It has built-in privacy by design, data minimization, auditability, and human review of AI-generated alerts. The study adopts a conceptual framework-development approach based on a targeted review of literature and policy sources. The framework is presented as a proposition for implementation and empirical evaluation, not as an experimentally validated system. Future research should assess its detection performance, false-positive rate, response latency, resource consumption, offline resilience, usability, and acceptance in rural Nigerian schools.