Cognitive Cyber Defense: AI–MIS Integration through Big Data and Cloud Frameworks for Next-Generation Digital Resilience

Abstract

The rapid rise in cyber threats across linked global digital ecosystems calls for a unified, intelligence-based defense strategy that brings together cybersecurity, management information systems (MIS), big-data analytics, and flexible IT governance. This study builds on the work of Kaur et al. (2023), Hasan et al. (2023), Mahmud et al. (2023), and Das et al. (2023) to create a comprehensive framework that uses artificial intelligence (AI), cloud computing, and data-driven decision-making to make digital systems more resilient. The research formulates an integrated AI–MIS Cyber-Defense Framework via a meta-synthesis of present empirical studies, clarifying the interaction among machine-learning analytics, predictive threat intelligence, and adaptive governance feedback loops. These interdependencies together improve the accuracy of detection, the ability to understand the issue in context, and the ability of organizations to adjust in unstable cyber environments. Quantitative evaluation shows that the system works better than traditional control systems. The average detection area under the curve (AUC) is over 0.93, the precision–recall metrics are above 0.90, and the composite resilience index is 27 percent higher. These results show that AI-enhanced MIS systems greatly improve cybersecurity readiness at both the national and business levels by allowing for proactive risk management, automated response coordination, and governance based on resilience. The proposed paradigm enhances the theoretical framework of cyber-resilience informatics and offers practical guidance for chief information officers (CIOs), cybersecurity strategists, and digital transformation leaders aiming to integrate scalable, self-optimizing, and AI-governed security measures into intricate digital infrastructures.