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
[1] Kant, D., & Johannsen, A. (2022). Evaluation of AI-based use 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 COVID19 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 StrategiesFuture 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] 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 [24] 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.
[25] 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..
[26] 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.
[27] 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.
[28] SANUSI, B. O. (2022). Sustainable Stormwater Management: Evaluating the Effectiveness of Green Infrastructure in Midwestern Cities. Well Testing Journal, 31(2), 74-96.
[29] 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.
[30] 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
[31] 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
[32] 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
[33] 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 [34] 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
[35] 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.
[36] 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.
[37] Aramide, O. O. (2022). AI-Driven Cybersecurity: The DoubleEdged Sword of Automation and Adversarial Threats.
International Journal of Humanities and Information Technology, 4(04), 19-38.
[38] Natarajan, D. R., & Kurunthachalam, A. (2018). Efficient Remote Patient Monitoring Using Multi-Parameter Devices and Cloud with Priority-Based Data Transmission Optimization. IndoAmerican Journal of Life Sciences and Biotechnology, 15(3), 112-121.
[2] Drydakis, N. (2022). Artificial Intelligence and reduced SMEs’ business risks. A dynamic capabilities analysis during the COVID19 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 StrategiesFuture 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] 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 [24] 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.
[25] 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..
[26] 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.
[27] 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.
[28] SANUSI, B. O. (2022). Sustainable Stormwater Management: Evaluating the Effectiveness of Green Infrastructure in Midwestern Cities. Well Testing Journal, 31(2), 74-96.
[29] 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.
[30] 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
[31] 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
[32] 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
[33] 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 [34] 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
[35] 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.
[36] 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.
[37] Aramide, O. O. (2022). AI-Driven Cybersecurity: The DoubleEdged Sword of Automation and Adversarial Threats.
International Journal of Humanities and Information Technology, 4(04), 19-38.
[38] Natarajan, D. R., & Kurunthachalam, A. (2018). Efficient Remote Patient Monitoring Using Multi-Parameter Devices and Cloud with Priority-Based Data Transmission Optimization. IndoAmerican Journal of Life Sciences and Biotechnology, 15(3), 112-121.