{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:09:17Z","timestamp":1758672557692,"version":"3.44.0"},"reference-count":0,"publisher":"Advances in Artificial Intelligence and Machine Learning","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAIML"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>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\u2019s application through real-world-inspired scenarios and presents criteria for evaluating AI\/ML deployment readiness across infrastructure sectors.<\/jats:p>","DOI":"10.54364\/aaiml.2025.53228","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T07:20:02Z","timestamp":1758612002000},"page":"4053-4073","source":"Crossref","is-referenced-by-count":0,"title":["The AIM-PRISM Framework: A Novel Strategic Model for Machine Learning and Artificial Intelligence Deployment in National Infrastructure Cybersecurity"],"prefix":"10.54364","volume":"05","author":[{"given":"Mansoor","family":"G. Al-Thani","sequence":"first","affiliation":[]}],"member":"32807","published-online":{"date-parts":[[2025]]},"container-title":["Advances in Artificial Intelligence and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/576253228.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T07:20:03Z","timestamp":1758612003000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/576253228.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":0,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.54364\/aaiml.2025.53228","relation":{},"ISSN":["2582-9793"],"issn-type":[{"value":"2582-9793","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}