A Multi-Layered Ai-Based Cybersecurity Framework for Business Continuity and Operational Resilience
Year: 2026
Type: article
Study design: Observational Study
Abstract: Organisations today operate in a threat environment in which cyberattacks are no longer isolated technical incidents but direct threats to business continuity and operational resilience. This study proposes and empirically examines a multi-layered, artificial-intelligence-based cybersecurity framework structured around three defense layers a predictive/preventive layer, a detective layer, and a responsive/recovery layer and evaluates the influence of each layer on organisational business continuity and operational resilience. Anchored on Defence-in-Depth theory, the study formulated three objectives, three research questions and three corresponding hypotheses. A descriptive survey design was adopted, involving 230 purposively sampled information technology, risk and operations personnel across medium and large organisations. Data were analysed using descriptive statistics and chi-square tests of association at the 0.05 level of significance. Findings show that all three layers of the proposed framework predictive/preventive, detective, and responsive/recovery exert a statistically significant influence on business continuity and operational resilience, with the responsive/recovery layer recording the strongest observed effect. The study concludes that resilience is best secured not through any single AI-security control but through the deliberate, coordinated layering of predictive, detective and responsive AI capabilities across the organisation. Recommendations are offered for organisational leaders, regulators and institutions engaged in cybersecurity capacity-building. Keywords: Artificial intelligence; Multi-layered cybersecurity; Defence-in-depth; Business continuity; Operational resilience
Institutions: Baze University, University of Uyo, University of Abuja
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7208826122
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