RISKS OF IMPLEMENTING AND USING ARTIFICIAL INTELLIGENCE BY OIL AND GAS SECTOR ENTERPRISES
DOI:
https://doi.org/10.15330/apred.2.21.333-343Keywords:
artificial intelligence, risk management, oil and gas sector, digital transformation, cybersecurity, AI ethicsAbstract
The article is devoted to the study of risks associated with the implementation and use of artificial intelligence (AI) by enterprises in Ukraine’s oil and gas sector. The purpose of the article is to identify the key threats arising in the process of digital transformation of the industry, classify and assess these threats, and develop strategies for managing such risks to ensure the safe, effective, and ethical application of AI technologies.
The research methodology is based on risk analysis methods in accordance with the international ISO 31000 standard, including both qualitative and quantitative risk assessment, the construction of a risk matrix, as well as methods of theoretical generalization, comparative analysis, systematization, and expert evaluation. Scenario modeling tools were applied to predict the potential consequences of risk realization.A systematic approach was applied to assess the risk levels of using AI technologies in oil and gas enterprises.
The study revealed that most risks fall within a high threat level (6–9 points), requiring immediate response. The most critical risks were identified as increased vulnerability to cyberattacks, unreliability of algorithms, data breaches, and social risks related to job reductions and the need for personnel retraining.
A set of strategic recommendations was formulated, covering technical, organizational, legal, economic, and social aspects of risk management. To manage the risks associated with the adoption and use of AI in the oil and gas sector effectively, a comprehensive approach is proposed. This includes phased algorithm testing, ensuring transparency of AI decision-making (Explainable AI), data and access audits, strengthening cybersecurity measures, developing ethical AI codes, implementing retraining programs, and updating the regulatory framework.
The scientific novelty of the research lies in adapting risk management principles to the specifics of a high-risk industry under conditions of digital transformation and in developing a detailed risk matrix that considers technical, informational, economic, social, environmental, legal, and ethical aspects of AI implementation.
The practical significance of the study lies in the development of specific recommendations for industry enterprises aimed at minimizing risks and ensuring the safe and effective use of AI—an especially relevant issue in the context of global competition, cyber threats, and sustainable development requirements.
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