Cyber Risk Quantification for SMEs: AI-Based Approaches to Enhance Resilience
Main Article Content
Abstract
Small and medium-sized enterprises (SMEs) are particularly susceptible to cyber threats, and it has been discovered that SMEs are nearly 60 times more vulnerable to a breach than large corporations, and that the average cost of a data breach globally is nearly fourfold in 2022 to USD 4.35 million. Despite their contribution to the economy of nations across the globe, SMEs normally lack the dedicated cybersecurity infrastructure and resources that larger organizations can easily purchase, which makes them highly vulnerable to operational and financial interference. The present paper presents a theoretically-grounded and framework-based study, which investigates the quantification of cyber risks with the use of AI on SMEs. The proposed structure will introduce the application of machine learning and predictive analytics within the risk scoring model into the framework in order to enable SMEs detect vulnerabilities, anticipate events and transform the risks linked to cybersecurity into a measurable business and financial impact. The model allows the leaders of the SMEs to possess actionable decision support to run and simultaneously provide insurers with clear and data-driven information concerning the organizational risk. In addition, the framework would focus on AI-driven risk scorecards that would contribute to the efficiency of the operations and aid in the utilization of cyber insurance. The study is also internationally relevant and it assists to build SME resilience by demonstrating how AI can deal with the resource shortage and more exposure to digital threats by proactively and scalably using competitiveness protection in a more interconnected economy.
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Ratnawat, C. (2023). Cyber Risk Quantification for SMEs: AI-Based Approaches to Enhance Resilience. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 15(01), 206-212. https://doi.org/10.18090/samriddhi.v15i01.36
Section
Review Article
References
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Interdisciplinary Studies, 3(04), 61-90.
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AI for social impact projects in Africa. International Journal of
Multidisciplinary Research and Growth Evaluation, 2(2), 307-318.
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Engineering & Technology Advancements (ESP-JETA), 1(1), 98-111.
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Artificial intelligence and information system resilience to cope
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4.0, disaster risk management and infrastructure resilience: a
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AI-Based Threat Detection Systems for Cloud Infrastructure:
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Intelligent Threat Detection and Adaptive Network Security
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31(1), 185-198.
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Building supply-chain resilience: an artificial intelligence-based
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Balzary, J., ... & Shen, X. (2021). AI as an Enabler for Long-Term
Resilience in Manufacturing.
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A. I., Adepoju, A. H., & Ubamadu, B. C. (2021). Review of
enterprise communication security architectures for improving
confidentiality, integrity, and availability in digital workflows.
IRE Journals, 5(5), 370-372.
[24] Shaik, Kamal Mohammed Najeeb. (2022). Security Challenges
and Solutions in SD-WAN Deployments. SAMRIDDHI A Journal
of Physical Sciences Engineering and Technology. 14. 2022.
10.18090/samriddhi.v14i04..
[25] Adebayo, Ismail Akanmu. (2022). ASSESSMENT OF
PERFORMANCE OF FERROCENE NANOPARTICLE -HIBISCUS
CANNABINUS BIODIESEL ADMIXED FUEL BLENDED WITH
HYDROGEN IN DIRECT INJECTION (DI) ENGINE. Transactions of
Tianjin University. 55. 10.5281/zenodo.16931428.
[26] Asamoah, A. N. (2022). Global Real-Time Surveillance of Emerging
Antimicrobial Resistance Using Multi-Source Data Analytics.
INTERNATIONAL JOURNAL OF APPLIED PHARMACEUTICAL
SCIENCES AND RESEARCH, 7(02), 30-37.
[27] SANUSI, B. O. (2022). Sustainable Stormwater Management:
Evaluating the Effectiveness of Green Infrastructure in
Midwestern Cities. Well Testing Journal, 31(2), 74-96.
[28] Sehgal, N., & Mohapatra, A. (2021). Federated Learning
on Cloud Platforms: Privacy-Preserving AI for Distributed
Data. International Journal of Technology, Management and
Humanities, 7(03), 53-67.
[29] Transforming Diagnostics Manufacturing at Cepheid: Migration
from Paper-Based Processes to Digital Manufacturing using
Opcenter MES. (2022). International Journal of Research and
Applied Innovations, 5(1), 9451-9456. https://doi.org/10.15662/
IJRAI.2022.0501005
[30] Venkata Krishna Bharadwaj Parasaram. (2022). Converging
Intelligence: A Comprehensive Review of AI and Machine
Learning Integration Across Cloud-Native Architectures.
International Journal of Research & Technology, 10(2), 29–34.
Retrieved from https://ijrt.org/j/article/view/749
[31] Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram.
(2019). Software-Centric Automation Frameworks Integrating
AI and Cybersecurity Principles. International Journal of
Engineering Science & Humanities, 9(1), 30–40. Retrieved from
https://www.ijesh.com/j/article/view/539
[32] Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021).
Autonomous Manufacturing Operations Using Intelligent MES
and Cloud-Native Analytics. Journal of Multidisciplinary
Knowledge, 1(1), 45–55. Retrieved from
https://jmk.datatablets.com/index.php/j/article/view/127
[33] Venkata Krishna Bharadwaj Parasaram. (2021). Assessing the
Impact of Automation Tools on Modern Project Governance.
International Journal of Engineering Science and Humanities,
11(4), 38–47. Retrieved from https://www.ijesh.com/j/article/
view/423
[34] Sunkara, G. (2022). The Role of AI and Machine Learning in
Enhancing SD-WAN Performance. SAMRIDDHI: A Journal of
Physical Sciences, Engineering and Technology, 14(04), 1-9.
[35] Lins, S., Pandl, K. D., Teigeler, H., Thiebes, S., Bayer, C., & Sunyaev,
A. (2021). Artificial intelligence as a service: classification and
research directions. Business & Information Systems Engineering,
63(4), 441-456.
[36] Aramide, O. O. (2022). AI-Driven Cybersecurity: The Double-
Edged Sword of Automation and Adversarial Threats.
International Journal of Humanities and Information Technology,
4(04), 19-38.
[37] Natarajan, D. R., & Kurunthachalam, A. (2018). Efficient Remote
Patient Monitoring Using Multi-Parameter Devices and Cloud
with Priority-Based Data Transmission Optimization. Indo-
American Journal of Life Sciences and Biotechnology, 15(3),
112-121.
cases for enhancing the cyber security defense of small and
medium-sized companies (SMEs). Electronic Imaging, 34, 1-8.
[2] Drydakis, N. (2022). Artificial Intelligence and reduced SMEs’
business risks. A dynamic capabilities analysis during the COVID-
19 pandemic. Information Systems Frontiers, 24(4), 1223-1247.
[3] Oladuji, T. J., Adewuyi, A. D. E. M. O. L. A., Nwangele, C. R., &
Akintobi, A. O. (2021). Advancements in financial performance
modeling for SMEs: AI-driven solutions for payment systems
and credit scoring. Iconic Research and Engineering Journals,
5(5), 471-486.
[4] AMS, N. (2022). Towards Resilient Manufacturing Ecosystems
Through Artificial Intelligence–Symposium Report.
[5] Rawindaran, N., Nawaf, L., Bentotahewa, V., Prakash, E., Jayal,
A., Hewage, C., & Alghazzawi, D. M. N. (2022). Detection and
Minimization of Malware by Implementing AI in SMEs. In
Malware-Detection and Defense. IntechOpen.
[6] Lau, H., Tsang, Y. P., Nakandala, D., & Lee, C. K. (2021). Risk
quantification in cold chain management: a federated learningenabled
multi-criteria decision-making methodology. Industrial
Management & Data Systems, 121(7), 1684-1703.
[7] Ruoslahti, H., & Davis, B. (2021). Societal impacts of cyber security
assets of project echo. WSEAS Transactions on Environment and
Development, 17, 1274-1283.
[8] Mou, A. J., Hossain, M. S., & Siddiqui, N. A. (2022). Digital
transformation in marketing: evaluating the impact of web
analytics and SEO on SME growth. American Journal of
Interdisciplinary Studies, 3(04), 61-90.
[9] Bianchini, M., & Kwon, I. (2021). Enhancing SMEs’ resilience
through digitalisation: The case of Korea.
[10] Nwangele, C. R., Adewuyi, A., Ajuwon, A., & Akintobi, A. O.
(2021). Advances in sustainable investment models: Leveraging
AI for social impact projects in Africa. International Journal of
Multidisciplinary Research and Growth Evaluation, 2(2), 307-318.
[11] Sharma, H. (2021). Next-generation firewall in the cloud: Advanced firewall solutions to the cloud. ESP Journal of
Engineering & Technology Advancements (ESP-JETA), 1(1), 98-111.
[12] Mohapatra, A., & Sehgal, N. (2018). Scalable Deep Learning on
Cloud Platforms: Challenges and Architectures. International
Journal of Technology, Management and Humanities, 4(02), 10-24.
[13] Gupta, S., Modgil, S., Meissonier, R., & Dwivedi, Y. K. (2021).
Artificial intelligence and information system resilience to cope
with supply chain disruption. IEEE Transactions on Engineering
Management, 71, 10496-10506.
[14] Kuzminykh, I., Ghita, B., Sokolov, V., & Bakhshi, T. (2021).
Information security risk assessment. Encyclopedia, 1(3), 602-617.
[15] Habibi Rad, M., Mojtahedi, M., & Ostwald, M. J. (2021). Industry
4.0, disaster risk management and infrastructure resilience: a
systematic review and bibliometric analysis. Buildings, 11(9), 411.
[16] Uddoh, J., Ajiga, D., Okare, B. P., & Aduloju, T. D. (2021).
AI-Based Threat Detection Systems for Cloud Infrastructure:
Architecture, Challenges, and Opportunities. Journal of Frontiers
in Multidisciplinary Research, 2(2), 61-67.
[17] Francis Onotole, E., Ogunyankinnu, T., Adeoye, Y., Osunkanmibi,
A. A., Aipoh, G., & Egbemhenghe, J. (2022). The Role of
Generative AI in developing new Supply Chain Strategies-
Future Trends and Innovations. International Journal of Supply
Chain Management, 11(4), 325-338.
[18] Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B. E.,
Kazancoglu, Y., & Narwane, V. (2022). Exploring the role of
artificial intelligence in managing agricultural supply chain
risk to counter the impacts of the COVID-19 pandemic. The
International Journal of Logistics Management, 33(3), 744-772.
[19] Sunkara, G. (2022). AI-Driven Cybersecurity: Advancing
Intelligent Threat Detection and Adaptive Network Security
in the Era of Sophisticated Cyber Attacks. Well Testing Journal,
31(1), 185-198.
[20] Sharkov, G., Todorova, C., & Varbanov, P. (2021). Strategies,
policies, and standards in the EU towards a roadmap for robust
and trustworthy AI certification. Information & Security, 50(1),
11-22.
[21] Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022).
Building supply-chain resilience: an artificial intelligence-based
technique and decision-making framework. International
journal of production research, 60(14), 4487-4507.
[22] Rauch, E., Acarkan, T., Alonso, J., Ansari, F., Athinarayanan, R.,
Balzary, J., ... & Shen, X. (2021). AI as an Enabler for Long-Term
Resilience in Manufacturing.
[23] Owobu, W. O., Abieba, O. A., Gbenle, P., Onoja, J. P., Daraojimba,
A. I., Adepoju, A. H., & Ubamadu, B. C. (2021). Review of
enterprise communication security architectures for improving
confidentiality, integrity, and availability in digital workflows.
IRE Journals, 5(5), 370-372.
[24] Shaik, Kamal Mohammed Najeeb. (2022). Security Challenges
and Solutions in SD-WAN Deployments. SAMRIDDHI A Journal
of Physical Sciences Engineering and Technology. 14. 2022.
10.18090/samriddhi.v14i04..
[25] Adebayo, Ismail Akanmu. (2022). ASSESSMENT OF
PERFORMANCE OF FERROCENE NANOPARTICLE -HIBISCUS
CANNABINUS BIODIESEL ADMIXED FUEL BLENDED WITH
HYDROGEN IN DIRECT INJECTION (DI) ENGINE. Transactions of
Tianjin University. 55. 10.5281/zenodo.16931428.
[26] Asamoah, A. N. (2022). Global Real-Time Surveillance of Emerging
Antimicrobial Resistance Using Multi-Source Data Analytics.
INTERNATIONAL JOURNAL OF APPLIED PHARMACEUTICAL
SCIENCES AND RESEARCH, 7(02), 30-37.
[27] SANUSI, B. O. (2022). Sustainable Stormwater Management:
Evaluating the Effectiveness of Green Infrastructure in
Midwestern Cities. Well Testing Journal, 31(2), 74-96.
[28] Sehgal, N., & Mohapatra, A. (2021). Federated Learning
on Cloud Platforms: Privacy-Preserving AI for Distributed
Data. International Journal of Technology, Management and
Humanities, 7(03), 53-67.
[29] Transforming Diagnostics Manufacturing at Cepheid: Migration
from Paper-Based Processes to Digital Manufacturing using
Opcenter MES. (2022). International Journal of Research and
Applied Innovations, 5(1), 9451-9456. https://doi.org/10.15662/
IJRAI.2022.0501005
[30] Venkata Krishna Bharadwaj Parasaram. (2022). Converging
Intelligence: A Comprehensive Review of AI and Machine
Learning Integration Across Cloud-Native Architectures.
International Journal of Research & Technology, 10(2), 29–34.
Retrieved from https://ijrt.org/j/article/view/749
[31] Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram.
(2019). Software-Centric Automation Frameworks Integrating
AI and Cybersecurity Principles. International Journal of
Engineering Science & Humanities, 9(1), 30–40. Retrieved from
https://www.ijesh.com/j/article/view/539
[32] Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021).
Autonomous Manufacturing Operations Using Intelligent MES
and Cloud-Native Analytics. Journal of Multidisciplinary
Knowledge, 1(1), 45–55. Retrieved from
https://jmk.datatablets.com/index.php/j/article/view/127
[33] Venkata Krishna Bharadwaj Parasaram. (2021). Assessing the
Impact of Automation Tools on Modern Project Governance.
International Journal of Engineering Science and Humanities,
11(4), 38–47. Retrieved from https://www.ijesh.com/j/article/
view/423
[34] Sunkara, G. (2022). The Role of AI and Machine Learning in
Enhancing SD-WAN Performance. SAMRIDDHI: A Journal of
Physical Sciences, Engineering and Technology, 14(04), 1-9.
[35] Lins, S., Pandl, K. D., Teigeler, H., Thiebes, S., Bayer, C., & Sunyaev,
A. (2021). Artificial intelligence as a service: classification and
research directions. Business & Information Systems Engineering,
63(4), 441-456.
[36] Aramide, O. O. (2022). AI-Driven Cybersecurity: The Double-
Edged Sword of Automation and Adversarial Threats.
International Journal of Humanities and Information Technology,
4(04), 19-38.
[37] Natarajan, D. R., & Kurunthachalam, A. (2018). Efficient Remote
Patient Monitoring Using Multi-Parameter Devices and Cloud
with Priority-Based Data Transmission Optimization. Indo-
American Journal of Life Sciences and Biotechnology, 15(3),
112-121.