The AIM-PRISM Framework: A Novel Strategic Model for Machine Learning and Artificial Intelligence Deployment in National Infrastructure Cybersecurity

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, Se-curity, and Meaningful interpretation (AIM-PRISM). This framework addresses operational, ethical, and governance considerations, offering a structured guide for policymakers, en-gineers, 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.