Autonomous Cybersecurity for Edge Devices in Remote Oil & Gas Operations: A Resilience Framework for Low-Connectivity Environments

Samuel Grant Quansah ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2025, vol. 8, issue 02, 65-80

Abstract: The remote operation of oil and gas fields is being transformed by access to edge computing or Industrial Internet of Things (IIoT) devices that allow real-time processing in harsh low-bandwidth areas. But this progress brings new almost-existentially perilous cyber security threats that classical cloud-centric security frameworks and models fail to take care of. In the current paper, an autonomous, resilience-driven cybersecurity model that incorporates endpoint hardening, local threat detection based on AI, decentralized authentication, opportunistic synchronization is proposed. Our model, unlike traditional models like NIST CSF, and IEC 62443 focuses on offline operation capabilities and security independence, which is essential to low-bandwidth oil and gas sites. Under a simulated pipeline project in a desert, the framework shortened the time it took to respond to attacks by 60 percent on an average during connectivity outages and lowered the unauthorized access to the system by 45 percent via decentralized authentication. The research provides practical solutions to securing edge computing in situations with the unreliability of connectivity, one of the emerging cybersecurity issues in the digital transformation of the critical infrastructure.

Keywords: Edge Computing Security; Remote Oil and Gas Operations; Low-Connectivity Cybersecurity; Industrial Cybersecurity Framework; Autonomous Threat Detection (search for similar items in EconPapers)
Date: 2025
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Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 is currently edited by Justyna Żywiołek

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