Intelligent Cybersecurity Frameworks for Data Protection in Cloud-Integrated Management Information Systems

Abstract

Enterprise workloads keep moving to public, private, and hybrid cloud environments, and that shift is widening the attack surface available to would-be intruders just as organizations lean harder on Management Information Systems (MIS) to run day-to-day decisions. This paper works through fifteen recent studies sitting at the crossing point of artificial intelligence (AI), cybersecurity, cloud computing, and MIS governance, and tries to say something coherent about what they add up to. Rather than proposing and testing one new tool, the review pulls out the themes that keep resurfacing, AI and machine-learning-based threat detection, cyber threat intelligence, big-data analytics, data governance, federated and privacy-preserving learning, sustainable data-center design, and the human side of security that technical papers tend to skip and organizes them into a five-layer conceptual framework meant to help later empirical work. The review follows an explicit search, screening, and synthesis process, described in Section II; each proposed framework layer is traced back, in the discussion itself, to the specific literature that motivates it, and a thematic distribution chart shows how attention is split across sub-topics in the corpus. What comes out of this is that detection capability and MIS governance are comparatively well covered, while a handful of cross-cutting issues, explainability at the implementation level, how employees behave once AI is in the loop, energy-aware security operations, and the practical limits of federated learning, are thinner than their real-world importance would suggest. The paper closes by naming its own limitations and laying out where empirical work still needs to happen.