Open Access

REVIEW

Cybersecurity Opportunities and Risks of Artificial Intelligence in Industrial Control Systems: A Survey

Ka-Kyung Kim, Joon-Seok Kim, Dong-Hyuk Shin, Ieck-Chae Euom^*

System Security Research Center, Chonnam National University, Gwangju, Republic of Korea

* Corresponding Author: Ieck-Chae Euom. Email:

(This article belongs to the Special Issue: The Evolution of Cybersecurity and AI: Surveys and Tutorials)

Computer Modeling in Engineering & Sciences 2026, 146(2), 5 https://doi.org/10.32604/cmes.2026.077315

Received 06 December 2025; Accepted 27 January 2026; Issue published 26 February 2026

Abstract

As attack techniques evolve and data volumes increase, the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential. Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems. However, its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm. To address these concerns, this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments, examining both their benefits and associated risks. A systematic literature review was conducted across major scientific databases, including IEEE, Elsevier, Springer Nature, ACM, MDPI, and Wiley, covering peer-reviewed journal and conference papers published between 2017 and 2026. Studies were selected based on predefined inclusion and exclusion criteria following a structured screening process. Based on an analysis of 101 selected studies, this survey categorizes artificial intelligence-based threat detection approaches across the physical, control, and application layers of industrial control systems and examines poisoning, evasion, and extraction attacks targeting industrial artificial intelligence. The findings identify key research trends, highlight unresolved security challenges, and discuss implications for the secure deployment of artificial intelligence-enabled cybersecurity solutions in industrial control systems.

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Supplementary Material

Supplementary Material File

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