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Volume 14
Issue 4
10.3390/technologies14040194
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Systematic Review

Integrating Machine Learning and Business Intelligence into Supply Chain Risk Management for a Comprehensive Cybersecurity Framework: A Systematic Literature Review

by
Rasha Aljaafreh
1,*,
Firas Al-Doghman
1,
Farookh Hussain
1,
Fazlullah Khan
2 and
Ali Aljaafreh
3
1
Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
2
Faculty of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
3
School of Business, Mutah University, Karak 61710, Jordan
*
Author to whom correspondence should be addressed.
Technologies 2026, 14(4), 194; https://doi.org/10.3390/technologies14040194
Submission received: 31 December 2025 / Revised: 4 March 2026 / Accepted: 10 March 2026 / Published: 24 March 2026
(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)
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Abstract

Supply chain cybersecurity is a growing concern for businesses as they utilize increasingly interconnected digital systems. This systematic literature review examines how machine learning (ML) and business intelligence (BI) may be used in conjunctions to improve supply chain cyber security risk management. This review followed PRISMA guidelines. A quality evaluation was performed based on CASP to evaluate 35 peer-reviewed articles published in 2016–2025. The review analysis indicates that although ML has been extensively utilized for threat detection, BI utilization is fragmented. Additionally, there is a lack of integrated ML-BI frameworks, specifically for small–medium enterprises (SMEs) and developing economies. As such, this literature review provides a conceptual four-layer framework of predictive and analytical capabilities for threat detection, risk assessment, and decision-making. It also identifies a structured research agenda with which to advance the field of research.
Keywords: supply chain cybersecurity; machine learning; business intelligence; systematic literature review; PRISMA; risk management; threat detection supply chain cybersecurity; machine learning; business intelligence; systematic literature review; PRISMA; risk management; threat detection

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MDPI and ACS Style

Aljaafreh, R.; Al-Doghman, F.; Hussain, F.; Khan, F.; Aljaafreh, A. Integrating Machine Learning and Business Intelligence into Supply Chain Risk Management for a Comprehensive Cybersecurity Framework: A Systematic Literature Review. Technologies 2026, 14, 194. https://doi.org/10.3390/technologies14040194

AMA Style

Aljaafreh R, Al-Doghman F, Hussain F, Khan F, Aljaafreh A. Integrating Machine Learning and Business Intelligence into Supply Chain Risk Management for a Comprehensive Cybersecurity Framework: A Systematic Literature Review. Technologies. 2026; 14(4):194. https://doi.org/10.3390/technologies14040194

Chicago/Turabian Style

Aljaafreh, Rasha, Firas Al-Doghman, Farookh Hussain, Fazlullah Khan, and Ali Aljaafreh. 2026. "Integrating Machine Learning and Business Intelligence into Supply Chain Risk Management for a Comprehensive Cybersecurity Framework: A Systematic Literature Review" Technologies 14, no. 4: 194. https://doi.org/10.3390/technologies14040194

APA Style

Aljaafreh, R., Al-Doghman, F., Hussain, F., Khan, F., & Aljaafreh, A. (2026). Integrating Machine Learning and Business Intelligence into Supply Chain Risk Management for a Comprehensive Cybersecurity Framework: A Systematic Literature Review. Technologies, 14(4), 194. https://doi.org/10.3390/technologies14040194

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