Multi-Cloud and Hybrid Cloud Security Frameworks
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I5P103Keywords:
Multi-Cloud Security, Hybrid Cloud Security, Cloud Security Architecture, Cloud Governance, Zero Trust Architecture (ZTA), Identity and Access Management (IAM), Data Encryption, Secure Cloud Migration, Cloud Compliance and Regulatory Standards, Cloud Risk Assessment, Security Orchestration and Automation, Cloud Threat Detection and Response, Secure API Management, Cloud Security Posture Management (CSPM), DevSecOps in Cloud Environments, Distributed Cloud Infrastructure, Virtual Private Cloud (VPC) Security, Container and Kubernetes Security, Cloud Access Security Broker (CASB), Incident Response in Multi-Cloud EnvironmentsAbstract
The rapid adoption of multi-cloud and hybrid-cloud architectures introduces significant security and governance complexities for organizations seeking agility and resilience. These deployments combine on-premises, private-cloud and multiple public-cloud services, creating a dispersed environment that challenges traditional perimeter-based security models. Research indicates that in multi-cloud environments, fragmentation of control, inconsistent policy enforcement, and visibility gaps increase vulnerability to misconfiguration, unauthorized access, and data breaches [9, 10]. Further, hybrid-cloud settings amplify these issues as enterprises must manage both legacy systems and diverse cloud platforms under unified governance. To address these evolving threats, this study evaluates existing security frameworks such as the Cloud Security Alliance’s Cloud Controls Matrix and the National Institute of Standards and Technology Cybersecurity Framework in the context of multi-provider, hybrid environments, assesses their strengths and gaps, and proposes an integrated AI-driven security framework tailored to hybrid/multi-cloud systems. The proposed model emphasizes unified identity and access management, encryption and key lifecycle across providers, continuous monitoring via machine-learning anomaly detection, and dynamic policy orchestration. Key implications suggest that enterprises adopting multi-cloud/hybrid strategies should prioritize interoperability and automation to sustain strong security postures while maintaining business agility. Directions for future research include empirical validation of the model via case studies and exploring the impact of emerging threat vectors (e.g., AI‐enabled attacks) on cloud-native frameworks.
References
[1] Aron, R., & Abraham, A. (2022). Resource scheduling methods for cloud computing environment: The role of meta-heuristics and artificial intelligence. Engineering Applications of Artificial Intelligence, 116, 105345. https://doi.org/10.1016/j.engappai.2022.105345
[2] Malekimajd, M., & Safarpoor-Dehkordi, A. (2022). A survey on cloud computing scheduling algorithms. Multiagent and Grid Systems, 18(2), 119-148. https://doi.org/10.3233/MGS-220217
[3] Tuli, S., Ilager, S., et al. (2020). Dynamic scheduling for cloud data centers using deep reinforcement learning. IEEE Transactions on Cloud Computing, 10(3), 233-245. https://doi.org/10.1109/TCC.2020.3041235
[4] Kranthi Kumar Routhu. (2020). Intelligent Remote Workforce Management: AI, Integration, and Security Strategies Using Oracle HCM Cloud. KOS Journal of AIML, Data Science, and Robotics, 1(1), 1–5. https://doi.org/10.5281/zenodo.17531257
[5] Padur, S. K. R. (2020). AI augmented disaster recovery simulations: From chaos engineering to autonomous resilience orchestration. International Journal of Scientific Research in Science, Engineering and Technology, 7(6), 367-378.
[6] Routhu, K. K. (2020). Strategic Compensation Equity and Rewards Optimization: A Multi-cloud Analytics Blueprint with Oracle Analytics Cloud. Available at SSRN 5737266.
[7] Padur, S. K. R. (2020). From centralized control to democratized insights: Migrating enterprise reporting from IBM Cognos to Microsoft Power BI. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, 6(1), 218-225.
[8] Arunagiri, R., & Vijayalakshmi, K. (2016). A comparative analysis of task scheduling algorithms in cloud computing environment. International Journal of Applied Engineering Research, 11(5), 3410-3416.
[9] Murad, S. A., Muzahid, A. J. M., Azmi, Z. R. M., Hoque, M. I., & Kowsher, M. (2022). A review on job scheduling technique in cloud computing and priority rule based intelligent framework. Journal of King Saud University - Computer and Information Sciences, 34(6, Part A), 2309-2331. https://doi.org/10.1016/j.jksuci.2022.03.027
[10] Routhu, K. K. (2019). Hybrid machine learning architecture for absence forecasting within Oracle Cloud HCM. KOS Journal of AIML, Data Science, and Robotics, 1(1), 1-5.
[11] Padur, S. K. R. (2019). Machine learning for predictive capacity planning: Evolution from analytical modeling to autonomous infrastructure. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 5(5), 285-293.
[12] Routhu, K. K. (2019). Conversational AI in Human Capital Management: Transforming Self-Service Experiences with Oracle Digital Assistant. International Journal of Scientific Research & Engineering Trends, 5(6).
[13] Routhu, K. K. (2019). AI-Enhanced Payroll Optimization: Improving Accuracy and Compliance in Oracle HCM. KOS Journal of AIML, Data Science, and Robotics, 1(1), 1-5.
[14] Thafzy. (2022). Machine learning (regression and clustering) for workload prediction and adaptive resource allocation. (Evaluation: iFogSim/SimGrid).
[15] Naji. (2022). Queuing theory for analyzing waiting times and resource allocation efficiency in multi-tenant cloud environments.
[16] Routhu, K. K. (2018). Reusable Integration Frameworks in Oracle HCM: Accelerating Enterprise Automation through Standardized Architecture. International Journal of Scientific Research & Engineering Trends, 4(4).
[17] Padur, S. K. R. (2018). Autonomous cloud economics: AI driven right sizing and cost optimization in hybrid infrastructures. International Journal of Scientific Research in Science and Technology, 4(5), 2090-2097.
[18] Reiss, C., Wilkes, J., & Hellerstein, J. L. (2012). Heterogeneity and dynamicity of clouds at scale: Google trace analysis. Proceedings of the 3rd ACM Symposium on Cloud Computing (SoCC '12). https://doi.org/10.1145/2391229.2391236
[19] Silva Filho, M. C., Oliveira, R. L., Monteiro, C. C., Inácio, P. R. M., & Freire, M. M. (2017). CloudSim Plus: A cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness. In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) (pp. 400-406). IEEE. https://doi.org/10.23919/INM.2017.7987304
[20] Yu, L., et al. (2022). A resource scheduling method for reliable and trusted composite service in container-cloud platforms. Frontiers in ICT, 9, Article 964784.
[21] Tuli, S., Casale, G., & Jennings, N. R. (2022). Learning to dynamically select cost-optimal schedulers in cloud computing environments. arXiv preprint.
[22] Routhu, K. K. (2022). From Case Management to Conversational HR: Redefining Help Desks with Oracle’s AI and NLP Framework. International Journal of Science, Engineering and Technology, 10(6).
[23] Vattikonda, N., Gupta, A. K., Polu, A. R., Narra, B., Buddula, D. V. K. R., & Patchipulusu, H. H. S. (2022). Blockchain Technology in Supply Chain and Logistics: A Comprehensive Review of Applications, Challenges, and Innovations. International Journal of Emerging Trends in Computer Science and Information Technology, 3(3), 72-80.
[24] Attipalli, A., BITKURI, V., Mamidala, J. V., Kendyala, R., & KURMA, J. (2022). Empowering Cloud Security with Artificial Intelligence: Detecting Threats Using Advanced Machine learning Technologies. Available at SSRN 5741263.
[25] Padur, S. K. R. (2022). Intelligent resource management: AI methods for predictive workload forecasting in cloud data centers. J. Artif. Intell. Mach. Learn. & Data Sci, 1(1), 2936-2941.
[26] Routhu, K. K. (2022). From RFID to Geofencing: IoT-Enabled Smart Time Tracking in Oracle HCM Cloud. International Journal of Science, Engineering and Technology, 10(4).
[27] Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2022). Data Security in Cloud Computing: Encryption, Zero Trust, and Homomorphic Encryption. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 31-41.
