An AI-Powered Cybersecurity Framework for Real-Time Threat Detection and Resilience in Oil and Gas Critical Infrastructure
Abstract: The rapid digital transformation of the oil and gas industry has accelerated the convergence of Information Technology (IT) and Operational Technology (OT), enabling enhanced operational efficiency, predictive maintenance, and remote asset management. While these technological advancements have improved industrial productivity, they have also introduced significant cybersecurity challenges by expanding the attack surface of critical infrastructure. Recent cyber incidents targeting industrial control systems have demonstrated that conventional cybersecurity solutions, which rely primarily on signature-based detection, static rule sets, and isolated security controls, are increasingly inadequate for combating sophisticated cyber threats such as Advanced Persistent Threats (APTs), ransomware, insider attacks, and zero-day exploits. Consequently, there is a growing need for intelligent cybersecurity frameworks capable of providing proactive threat detection, automated analysis, and adaptive response mechanisms. This study proposes an Artificial Intelligence (AI)-Powered Cybersecurity Framework designed to enhance cyber resilience in oil and gas critical infrastructure. The proposed framework integrates machine learning, deep learning, threat intelligence, Security Information and Event Management (SIEM), Security Orchestration, Automation and Response (SOAR), and internationally recognized cybersecurity standards into a unified architecture for real-time cyber threat detection and response. The framework adopts a layered architecture comprising data acquisition, data processing, AI analytics, decision intelligence, automated response, and governance layers, enabling continuous monitoring, predictive threat detection, and intelligent decision-making across both IT and OT environments. The framework was developed using a Design Science Research (DSR) methodology and validated through architectural mapping against the National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0), IEC 62443, ISO/IEC 27001, and the MITRE ATT&CK framework. The proposed architecture demonstrates how artificial intelligence can strengthen cybersecurity operations by improving threat visibility, reducing false-positive alerts, supporting automated incident response, and enhancing organizational cyber resilience. The study contributes to cybersecurity research by presenting a comprehensive AI-driven framework that integrates predictive analytics, intelligent automation, and governance into a single architecture tailored to industrial environments. Unlike many existing cybersecurity frameworks that emphasize governance or compliance, the proposed framework combines intelligent threat detection with operational resilience, offering practical guidance for organizations seeking to modernize cybersecurity capabilities within critical infrastructure sectors.