AI GOVERNANCE FRAMEWORKS FOR AI-DRIVEN NETWORK SECURITY AND CYBER RESILIENCE IN CRITICAL OIL AND GAS INFRASTRUCTURE

Abstract

As operational technology, industrial control systems and enterprise information technology have become more interconnected, the cyberattack surface of critical oil and gas infrastructure has dramatically grown. In today's era of increasingly complicated and prevalent cyber threats, artificial intelligence (AI) has demonstrated its transformative potential in bolstering network security, including through real-time threat detection, predictive analytics, automated incident response, and ongoing monitoring. However, the broad adoption of AI does present governance issues with regard to transparency, accountability, explainability, regulatory compliance, data integrity, and operational risk. The study delves into AI governance frameworks designed to facilitate the secure, ethical, and resilient adoption of AI-powered network security in critical oil and gas settings. The discussion focuses on governance principles which encourage responsible adoption of AI and harmonize cybersecurity activities with established industry best practices and risk management principles. It also delves into the trade-offs of automation and human supervision, aiming to mitigate risks from AI reliance and malicious use of automation. Focus is on building cyber resilience by implementing ongoing governance, lifecycle management and adaptive security controls that can adapt to changing threat environments. The study findings have provided recommendations for the strong inclusion of robust AI governance frameworks in cybersecurity plans across the oil and gas industry as a means to bolster organizational resiliency, augment decision-making processes, ensure regulatory compliance, and safeguard key energy assets from evolving cyber threats while promoting sustainable and trustworthy digital transformation.