AI-Assisted Cyber Resilience for Hazardous Gas Detection, CCTV, and Industrial Safety Monitoring Systems

As industrial safety systems become increasingly interconnected, the benefits of real-time visibility are accompanied by greater cybersecurity and operational risk. This paper proposes an AI-assisted Cyber Resilience model for hazardous gas detection, CCTV analytics, and industrial safety monitoring in high-risk facilities. This model is designed for high-risk industrial environments such as wellheads, petrochemical facilities, and GOSPs. It integrates multimodal safety evidence, including gas detection, video analytics, thermal imaging, and permit-control data, into a unified resilience framework, access control, digital work-permit systems, smart badges, QR-enabled certification records, air-quality monitoring (AQM)/weather stations, worker-health wearables, vehicle-permit data, and operational-technology telemetry. The paper synthesises evidence from 2020-2025 literature on real-time AI agents, industrial control security, gas-sensor analytics, computer-vision safety monitoring, and operational-technology governance. System effectiveness depends not only on model accuracy, but also on the integrity, availability, explainability, and recoverability of the full monitoring chain, but also on the integrity, availability, explainability, and recoverability of the entire monitoring chain. A resilient design therefore requires multimodal validation, edge inference, secure communications, tamper-evident evidence, human override, and staged response playbooks. The paper proposes an architecture that consolidates multimodal operational and safety signals into an AI-supported risk engine linked to OT security and safety response functions, an OT security operations function, and safety-instrumented response. It also offers a maturity model and six practical critical-area use cases covering digital work permits, working at height, confined-space entry, heavy lifting, worker safety and vehicle movement 1-8.