A Holistic AI and Cloud Governance Model for Demand Forecasting and Business Optimization with Risk Aware Cybersecurity
DOI:
https://doi.org/10.15662/IJEETR.2024.0605012Keywords:
AI governance, cloud governance, demand forecasting, business optimization, cyber risk management, predictive analytics, enterprise resilience, cloud securityAbstract
The rapid adoption of artificial intelligence (AI) and cloud computing has transformed enterprise operations, enabling real-time demand forecasting, business optimization, and advanced cyber risk management. However, organizations often face challenges in aligning AI-driven decision-making with cloud governance policies, compliance frameworks, and security protocols. This research proposes a holistic governance model integrating AI and cloud computing to optimize business processes while mitigating cyber risks. The model emphasizes the orchestration of predictive analytics for demand forecasting, AI-driven resource optimization, and robust security frameworks for proactive cyber risk management. By leveraging scalable cloud infrastructure, enterprises can ensure high availability, elasticity, and secure handling of sensitive data. The study combines theoretical frameworks, industry best practices, and case-based insights to validate the model’s effectiveness. Findings indicate that a unified AI-cloud governance framework enhances operational efficiency, reduces forecast errors, and strengthens organizational resilience against cyber threats. This research contributes to bridging the gap between emerging AI capabilities and cloud governance mechanisms, providing a structured approach for enterprises to achieve both operational excellence and cyber risk mitigation.
References
1. Davenport, T. H., & Harris, J. G. (2017). Competing on analytics: The new science of winning. Harvard Business School Press.
2. Natta, P. K. (2025). Scalable governance frameworks for AI-driven enterprise automation and decision-making. International Journal of Research Publications in Engineering, Technology and Management, 8(6), 13182–13193. https://doi.org/10.15662/IJRPETM.2025.0806022
3. Adari, V. K. (2024). APIs and open banking: Driving interoperability in the financial sector. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2015–2024.
4. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
5. Anumula, S. R. (2022). Governance frameworks for automated enterprise decision systems. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 137–157.
6. ISO/IEC. (2015). ISO/IEC 27001: Information security management systems. International Organization for Standardization.
7. Binu, C. T., Kumar, S. S., Rubini, P., & Sudhakar, K. (2024). Enhancing Cloud Security through Machine Learning-Based Threat Prevention and Monitoring: The Development and Evaluation of the PBPM Framework. https://www.researchgate.net/profile/Binu-C-T/publication/383037713_Enhancing_Cloud_Security_through_Machine_Learning-Based_Threat_Prevention_and_Monitoring_The_Development_and_Evaluation_of_the_PBPM_Framework/links/66b99cfb299c327096c1774a/Enhancing-Cloud-Security-through-Machine-Learning-Based-Threat-Prevention-and-Monitoring-The-Development-and-Evaluation-of-the-PBPM-Framework.pdf
8. M. A. Alim, M. R. Rahman, M. H. Arif, and M. S. Hossen, “Enhancing fraud detection and security in banking and e-commerce with AI-powered identity verification systems,” 2020.
9. Ramidi, M. (2022). Building secure biometric systems for digital identity verification in aviation mobile apps. International Journal of Engineering & Extended Technologies Research, 4(4), 5036–5047.
10. Lee, J., Bagheri, B., & Kao, H. A. (2015). Cyber-physical systems architecture for Industry 4.0 manufacturing. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001
11. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
12. Keezhadath, A. A., & Amarapalli, L. (2024). Ensuring Data Integrity in Pharmaceutical Quality Systems: A Risk-Based Approach. Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930), 1(1), 83-104.
13. Singh, A. (2020). Impact of network topology changes on performance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(4), 3687–3692. https://doi.org/10.15662/IJRPETM.2020.0304003
14. Gangina, P. (2022). Resilience engineering principles for distributed cloud-native applications under chaos. International Journal of Computer Technology and Electronics Communication, 5(5), 5760–5770.
15. Panda, M. R., & Kondisetty, K. (2022). Predictive Fraud Detection in Digital Payments Using Ensemble Learning. American Journal of Data Science and Artificial Intelligence Innovations, 2, 673-707.
16. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
17. Shapiro, C., & Varian, H. R. (1999). Information rules. Harvard Business School Press.
18. Sommer, R., & Paxson, V. (2010). Outside the closed world: Using machine learning for network intrusion detection. IEEE Symposium on Security and Privacy, 305–316. https://doi.org/10.1109/SP.2010.25
19. Udayakumar, R., Joshi, A., Boomiga, S. S., & Sugumar, R. (2023). Deep fraud Net: A deep learning approach for cyber security and financial fraud detection and classification. Journal of Internet Services and Information Security, 13(3), 138-157.
20. Kesavan, E. (2022). Driven learning and collaborative automation innovation via Trailhead and Tosca user groups. International Scientific Journal of Engineering and Management, 1(1), Article 00058. https://doi.org/10.55041/ISJEM00058
21. Pimpale, S. (2025). A Comprehensive Study on Cyber Attack Vectors in EV Traction Power Electronics. arXiv preprint arXiv:2511.16399.
22. Kusumba, S. (2023). A Unified Data Strategy and Architecture for Financial Mastery: AI, Cloud, and Business Intelligence in Healthcare. International Journal of Computer Technology and Electronics Communication, 6(3), 6974-6981.
23. Chivukula, V. (2020). IMPACT OF MATCH RATES ON COST BASIS METRICS IN PRIVACY-PRESERVING DIGITAL ADVERTISING. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 3(4), 3400-3405.
24. Joseph, J. (2023). Trust, but Verify: Audit-ready logging for clinical AI. https://www.researchgate.net/profile/Jimmy-Joseph-9/publication/395305525_Trust_but_Verify_Audit-ready_logging_for_clinical_AI/links/68bbc5046f87c42f3b9011db/Trust-but-Verify-Audit-ready-logging-for-clinical-AI.pdf
25. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
26. Sriramoju, S. (2022). Automated migration frameworks for legacy systems: A security-driven approach. International Journal of Computer Technology and Electronics Communication (IJCTEC), 5(3), 5146–5157.
27. von Solms, R., & van Niekerk, J. (2013). From information security to cyber security. Computers & Security, 38, 97–102. https://doi.org/10.1016/j.cose.2013.04.004
