AI-Driven Cybersecurity and Big Data-Enabled MIS Frameworks: Strengthening Supply Chain Integrity, Energy Resilience, and Critical Infrastructure Protection

Abstract

This study adopts a conceptual and analytical approach to examine the intersection of AI-driven cybersecurity, big data analytics, and MIS frameworks. The rapid evolution of cyber threats, combined with the exponential growth of big data. It puts a great challenge on the security, resilience, and efficiency of current supply chains and critical infrastructure systems. Cybersecurity AI-based and integrated with Management Information Systems. It offers a transformative approach to enhancing organizational defense mechanisms. AI enables enterprises to strengthen supply chain integrality, improve energy resilience, and ensure robust protection of critical infrastructures. It explores how AI algorithms, machine learning models, and anomaly detection systems work. It can process large volumes of supply chain and energy sector data to predict vulnerabilities and mitigate cyber risks. The proposed MIS framework integrates cybersecurity protocols, data-driven insights, and adaptive decision-making strategies to create a holistic model that addresses both operational efficiency and resilience against cyberattacks. The findings suggest that AI-enabled MIS frameworks significantly improve organizational preparedness, resilience, and adaptability in the face of evolving cyber threats. Enhanced supply chain integrality reduces disruptions, while energy resilience ensures sustainable operations under stress conditions. This framework provides a strategic blueprint for governments, businesses, and energy stakeholders aiming to align cybersecurity initiatives with sustainable development and digital transformation goals.