AI-DRIVEN CYBER THREAT INTELLIGENCE FRAMEWORK FOR CRITICAL DIGITAL INFRASTRUCTURE PROTECTION IN PAKISTAN

Abstract

The growing dependence on digital technologies has increased the vulnerability of critical infrastructure to sophisticated cyber threats. Traditional cybersecurity approaches are often inadequate in addressing rapidly evolving attack techniques, creating a need for proactive and intelligence-driven security solutions. This study developed and validated an AI-Driven Cyber Threat Intelligence (CTI) Framework for Critical Digital Infrastructure Protection in Pakistan. Grounded in Dynamic Capabilities Theory, the framework examined the effects of AI-Powered Threat Detection, Predictive Threat Analytics, Automated Incident Response, and Threat Intelligence Sharing on Cyber Threat Intelligence Effectiveness and Critical Digital Infrastructure Protection, with Cybersecurity Governance serving as a moderating factor. A quantitative cross-sectional survey was conducted among 387 cybersecurity professionals from critical infrastructure sectors in Pakistan. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that all AI-enabled cybersecurity capabilities significantly enhanced CTI Effectiveness, which in turn positively influenced Critical Digital Infrastructure Protection. Predictive Threat Analytics emerged as the strongest predictor, while Cybersecurity Governance strengthened the relationship between CTI Effectiveness and infrastructure protection.

The study highlights the strategic role of AI-driven cyber threat intelligence in enhancing cyber resilience and provides practical guidance for organizations and policymakers seeking to protect critical digital infrastructure in Pakistan