Artificial Intelligence (AI) is increasingly being deployed across the oil and gas industry to support environmental monitoring, predictive risk analysis, spill detection, and automated regulatory reporting. While these technologies present opportunities to significantly improve operational transparency and regulatory compliance, they also introduce new governance challenges, particularly in jurisdictions where environmental accountability and data transparency remain ongoing concerns. Building on earlier research examining the ethical implications of AI in extractive industries, this paper explores the governance structures required to ensure responsible AI deployment within oil and gas regulatory ecosystems. Using Nigeria's petroleum regulatory framework as a case study, the study examines how AI-enabled systems—including natural language processing models for regulatory document analysis and machine learning tools for environmental incident monitoring—can strengthen compliance oversight and improve the detection of environmental violations such as oil spills and reporting inconsistencies. However, the integration of AI into regulatory processes also raises concerns regarding algorithmic bias, data governance integrity, explainability of automated decision systems, and institutional dependence on opaque technological infrastructures. This paper proposes a governance-centered framework for AI adoption in extractive industries that integrates legal oversight, transparent algorithmic auditing, and responsible data management practices. By situating AI deployment within broader regulatory and governance structures, this research highlights the critical role of law and policy in shaping accountable and trustworthy AI systems capable of enhancing environmental stewardship, regulatory compliance, and public trust in resource-dependent economies.
