Abstract

The increasing intricacy and prevalence of online threats, growing complexity and frequency of cyber threats, particularly those targeting energy grids, transport systems, and financial platforms, necessitate a holistic approach to integrating intelligent technologies. This research proposes the AIM-PRISM framework, a strategic and adaptable model for deploying Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity for national infrastructure protection. While significant advancements have been made in incident response, AI-driven risk detection, and data protection, a unified deployment strategy is still lacking. Building on an extensive literature review, we identify key technological developments and implementation challenges and synthesize them into a novel eight-component framework: Adaptability, Integration, Monitoring, Predictive capacity, Responsiveness, Inclusivity, Security, and Meaningful interpretation (AIM-PRISM). This framework addresses operational, ethical, and governance considerations, offering a structured guide for policymakers, engineers, and organizational leaders. The research illustrates the framework’s application through real-world-inspired scenarios and presents criteria for evaluating AI/ML deployment readiness across infrastructure sectors.

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