ASSESSMENT OF THE EFFECTIVENESS OF INTERNAL CONTROL SYSTEMS IN THE CONTEXT OF ARTIFICIAL INTELLIGENCE AND PROCESS AUTOMATION
Keywords:
artificial intelligence; internal control; process automation; risk management; intelligent automated controls; human oversight; European Regulation on Artificial Intelligence (AI Act); COSO; COBIT; internal audit; regulatory compliance; corporate governance.Abstract
This study is dedicated to assessing the effectiveness of internal control systems
in the context of the increasing use of artificial intelligence and automation of business processes.
The transformation of traditional control mechanisms in a digital environment is analyzed,
emphasizing the role of intelligent automated controls, algorithmic risk and the need for human
oversight in decision-making.
Particular attention is paid to the new European regulatory framework for the use of
artificial intelligence - Regulation (EU) 2024/1689, which introduces a risk-based approach,
mandatory requirements for transparency, traceability and human control, as well as significant
sanctions for non-compliance. The study considers artificial intelligence not only as a tool for
increasing efficiency, but also as an object of internal control, subject to systematic management,
monitoring and audit.
Based on the principles of COSO and COBIT, an integrated approach is proposed for
assessing the effectiveness of internal control in an AI environment, which combines organizational,
technological and regulatory elements. Practical examples illustrate how the lack of adequate
internal controls over AI can lead to significant regulatory and financial risks, including the
imposition of significant sanctions.
The study highlights the importance of an adaptive and proactive approach to internal
control and audit in the context of digitalization, as a factor for resilience, compliance and good
corporate governance in modern organizations.
References
1. Joshi, R. (2022). Impact of Digital Transformation on IA Companies.. https://doi.org/
10.46254/in02.20220338
2. Kaya, C. (2025). Intelligent Environmental Control in Plant Factories: Integrating
Sensors, Automation, and AI for Optimal Crop Production. Food and Energy Security, 14(1).
https://doi.org/10.1002/fes3.70026
3. Huang, R. (2025). Internal Control and Risk Management in Accounting Information
System. International Journal of Information systems in the Service Sector, 16(1), 1-17. https://
doi.org/10.4018/ijisss.392475
4. Piechowski, M. (2025). Digital transformation - breaking down barriers to adaptive
control of production processes.. Journal of Physics Conference Series, 3160(1), 012012. https://
doi.org/10.1088/1742-6596/3160/1/012012
5. Volosova, A. and Matiukhina, E. (2020). Using artificial intelligence for effective
decision-making in corporate governance under conditions of deep uncertainty. SHS Web of
Conferences, 89, 03008. https://doi.org/10.1051/shsconf/20208903008
6. Wang, M. (2024). Artificial intelligence empowers the construction of first-class financial
management system. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/
amns-2024-0518
7. Enhancing Corporate Governance through Robust Internal Control Mechanisms.
Advances in management & financial Reporting, 2(2), 72-84. https://doi.org/10.60079/
amfr.v2i2.173
8. Luo, X., Cheng, Y., & Liao, Z. (2024). Introduction to the Special Issue on Machine
Learning-Guided Intelligent Modeling with Its Industrial Applications. Computer Modeling in
Engineering & Sciences, 141(1), 7-11. https://doi.org/10.32604/cmes.2024.056214
9. Budaev, S., Cusimano, G., & Rønnestad, I. (2025). FishMet: A Digital Twin Framework
for Appetite, Feeding Decisions and Growth in Salmonid Fish. Aquaculture Fish and Fisheries, 5(2).
https://doi.org/10.1002/aff2.70064
10. Papadopoulos, A. (2025). Integrity Versus Ideology in Automated Assessment:
The Jobseeker Snapshot. Australian Journal of Social Issues, 60(2), 418-427. https://doi.org/
10.1002/ajs4.70007
11. Huang, R. (2025). Internal Control and Risk Management in Accounting Information
System. International Journal of Information systems in the Service Sector, 16(1), 1-17. https://
doi.org/10.4018/ijisss.392475
12. Alnemari, A. (2025). Developing highly accurate machine learning models for
optimizing water quality management decisions in tilapia aquaculture. Scientific Reports, 15(1).
https://doi.org/10.1038/s41598-025-16939-w
13. Allen, J. (2025). Fostering and Cultivating Human‐AI Collaboration and Partnerships in
an Evolving Workplace. Proceedings of the Association for Information Science and Technology,
62(1), 1202-1205. https://doi.org/10.1002/pra2.1365
14. Fukukawa, K. and Trivedi, R. (2025). Empathy, Ethics and Efficacy: The 3Es of
Implementing Artificial Intelligence for Consumer Encounters. Psychology and Marketing, 42(9),
2352-2368. https://doi.org/10.1002/mar.22235
15. Jian, L., Shen, L., & Huang, W. (2025). Navigating Copyright Risk and Governance
Challenges in Artificial Intelligence Development: A Case Study From China. Journal of
International Development, 37(5), 1168-1193. https://doi.org/10.1002/jid.4007
16. Golpayegani, D., Hupont, I., Panigutti, C., Pandit, H., Schade, S., O’Sullivan, D., … &
Lewis, D. (2024). AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk
Documentation Inspired by the EU AI Act., 48-72. https://doi.org/10.1007/978-3-031-68024-3_3
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