Cyber Physical Security Framework for AI-Driven Digital Oilfield Architectures

The digital transformation of oil and gas operations has resulted in the evolution of AI-driven digital oilfields integrating SCADA systems, programmable logic controllers (PLCs), distributed control systems (DCS), Industrial Internet of Things (IIoT) devices, edge computing nodes, cloud-based analytics, and digital twin platforms. While these technologies significantly enhance operational efficiency, predictive analytics, and production optimization, they simultaneously expand the cyber-attack surface of critical oil and gas infrastructure. Offshore platforms and onshore processing facilities are increasingly exposed to cyber-physical threats that can disrupt production, compromise safety systems, and cause severe economic losses. This study proposes a layered cyber-physical security framework tailored specifically for AI-enabled digital oilfield architectures. The framework integrates network segmentation based on ISA/IEC 62443 principles, zero-trust access control, AI-based anomaly detection for industrial traffic, digital twin integrity validation, and resilience-based incident response modelling. A quantitative risk propagation model is developed to evaluate the impact of cyber events on critical assets. Simulation results demonstrate that the proposed framework reduces intrusion detection latency and significantly improves system resilience compared to traditional perimeter-based security approaches. The proposed architecture provides a scalable and practical strategy for securing next-generation digital oilfields while maintaining real-time performance requirements.