Adaptive Cloud Cybersecurity Architectures for Real-Time Threat Detection and Compliance Monitoring

Ramineni Damodaram; Ramineni Damodaram

doi:10.15662/IJSRAT.2025.0806010

Adaptive Cloud Cybersecurity Architectures for Real-Time Threat Detection and Compliance Monitoring

Ramineni Damodaram

Abstract

Cloud computing has transformed the digital ecosystem by enabling scalable, flexible, and cost-efficient infrastructures for organizations across industries. However, the increasing adoption of cloud technologies has also intensified cybersecurity challenges, including sophisticated cyberattacks, unauthorized access, ransomware, insider threats, and compliance violations. Traditional security mechanisms often fail to provide dynamic protection against rapidly evolving threats in distributed cloud environments. This research explores adaptive cloud cybersecurity architectures designed for real-time threat detection and compliance monitoring. The study emphasizes the integration of artificial intelligence, machine learning, behavioral analytics, automation, and zero-trust security principles to enhance cloud resilience. Adaptive architectures continuously monitor cloud infrastructures, analyze network behavior, identify anomalies, and respond automatically to potential threats while ensuring regulatory compliance with standards such as GDPR, HIPAA, ISO 27001, and PCI-DSS. The research further investigates the role of Security Information and Event Management systems, cloud-native security tools, and automated incident response mechanisms in minimizing security breaches and operational risks. A comprehensive methodology involving qualitative and quantitative analysis is proposed to evaluate the effectiveness, scalability, and responsiveness of adaptive cloud security frameworks. The findings suggest that adaptive cybersecurity architectures significantly improve threat visibility, reduce response times, enhance compliance management, and strengthen organizational security posture. The study contributes to the development of intelligent and proactive cloud security solutions capable of addressing modern cybersecurity challenges in highly dynamic cloud environments

Article Information

Journal	International Journal of Science, Research and Technology
Volume (Issue)	Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT)
DOI	https://doi.org/10.15662/IJSRAT.2025.0806010
Pages	15369-15379
Published	December 15, 2025
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Ramineni Damodaram (%2025). Adaptive Cloud Cybersecurity Architectures for Real-Time Threat Detection and Compliance Monitoring. International Journal of Science, Research and Technology , Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 15369-15379. https://doi.org/10.15662/IJSRAT.2025.0806010

References

1. Bansal, I. (2024). Next-gen cloud security operations: Real-time monitoring and automated incident response. International Journal of Computational and Experimental Science and Engineering. https://doi.org/10.22399/ijcesen.4454
2. Gandikota, S. R. (2024). Adaptive cyber threat detection using hybrid deep learning models in multi-cloud environments. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 4229–4238.
3. Jeyaram, A., & Muthukumaravel, A. (2024). Adaptive machine learning-driven cybersecurity: Enhancing real-time threat detection and response. In Proceedings of the IEEE International Conference on Intelligent Cyber Security Engineering Systems. IEEE. https://doi.org/10.1109/icses63760.2024.10910847
4. Krishnappa, M. S., Veerapaneni, P. K., Harve, B. M., & others. (2024). Cybersecurity in the cloud era: Protecting virtualized environments against evolving threats. In Proceedings of the International Conference on Intelligent Cybernetics Technology & Applications. IEEE. https://doi.org/10.1109/ICICYTA64807.2024.10913114
5. Lilhore, U. K., Simaiya, S., Alroobaea, R., Baqasah, A. M., Alsafyani, M., Alhazmi, A., & Khan, M. M. (2025). SmartTrust: A hybrid deep learning framework for real-time threat detection in cloud environments using Zero-Trust Architecture. Journal of Cloud Computing, 14(35). https://doi.org/10.1186/s13677-025-00764-7
6. Mehta, G., Jayaram, V., Maruthavanan, D., Jayabalan, D., Parthi, A. G., Bidkar, D. M., Pothineni, B., & Veerapaneni, P. K. (2024). Emerging cybersecurity architectures and methodologies for modern threat landscapes. International Journal of Computer Science and Information Technology Research. https://doi.org/10.5281/zenodo.14275106
7. Park, H., Azzaoui, A. E., & Park, J. H. (2025). AIDS-based cyber threat detection framework for secure cloud-native microservices. Electronics, 14(2), 229. https://doi.org/10.3390/electronics14020229
8. Sengupta, A., Tewari, R., Singh, H., Verma, A. K., & Bhatia, V. S. (2024). API gateway as a security sentinel: Adaptive threat detection at the edge of cloud services. Journal of Electrical Systems. https://doi.org/10.52783/jes.9016
9. Songa, A. V., & Karri, G. R. (2024). An integrated SDN framework for early detection of DDoS attacks in cloud computing. Journal of Cloud Computing, 13(64). https://doi.org/10.1186/s13677-024-00625-9
10. AzithTejaGanti, V. K., Senthilkumar, K. P., Robinson, T. L., Karunakaran, S., Pandugula, C., & Khatana, K. (2025). Energy-efficient real-time hybrid deep learning framework for adaptive IoT intrusion detection with scalable and dynamic threat mitigation. Proceedings of the International Conference on Optimization Techniques in Engineering. SSRN.
11. Bellundagi, M. (2024). A Multi-Layer AI-Driven Decision Intelligence Framework for Enterprise and Healthcare System. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11679-11687.
12. Appani, C. (2025). AI-powered threat detection in real-time payment systems. International Journal of Environmental Sciences, 11(19s), 22–27. https://doi.org/10.64252/9yf23877
13. Narayanan, S. (2024). Authenticity assurance architecture: A multi-layer organizational deepfake threat taxonomy and control framework. World Journal of Advanced Research and Reviews, 24(3), 3639–3647. https://philarchive.org/archive/NARAAA-3
14. Adepu, R. (2022). Building secure multi-cloud infrastructure for mission-critical enterprise workloads. The International Journal of Research Publications in Engineering, Technology and Management, 5(5), 14–32.
15. Parupalli, A. (2025, November). Predicting Customer Satisfaction Through Sentiment Analysis in CRM Using Machine Learning. In 2025 5th International Conference on Artificial Intelligence and Signal Processing (AISP) (pp. 1-5). IEEE.
16. Boddupally, H. L. (2024). Embedding Governance into LLM Workflow Architectures for Enterprise-Wide Automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(7), 279-294.
17. Mulla, F. A. (2024). Modern Mobile Testing Tools: A Comprehensive Guide to Quality Assurance and Automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 10-32628.
18. Kunadi, S. K. (2022). Building scalable master data management systems for enterprise data platforms. International Journal of Computer Technology and Electronics Communication (IJCTEC), 5(2), 4830–4843.
19. Raja, G. V. (2023). AI Driven Secure Intelligent Framework for Fraud Detection Cybersecurity and Cloud Based Enterprise Systems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9068-9076.
20. Soundappan, S. J. (2025). Privacy Preserving Data Analytics Frameworks using Homomorphic Encryption Techniques. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 14531.
21. Macha, Y., & Pulichikkunnu, S. K. (2023). An Explainable AI System for Fraud Identification in Insurance Claims via Machine-Learning Methods. Int. J. Adv. Res. Sci. Commun. Technol, 3(3), 1391-1400.
22. Patel, M., & Chaturvedi, V. (2025). A survey on artificial intelligence techniques for disease prediction in healthcare. ESP Journal of Engineering & Technology Advancements, 5(4), 201–210.
23. Sengupta, J., & Alzbutas, R. (2022). Intracranial hemorrhages segmentation and features selection applying cuckoo search algorithm with gated recurrent unit. Applied Sciences, 12(21), 10851.
24. Lanka, S. (2023). Built for the Future How Citrix Reinvented Security Monitoring with Analytics. International Journal of Humanities and Information Technology, 5(02), 26-33.
25. Anand, L. (2024). AI-Powered Cloud Cybersecurity Architecture for Risk Prediction and Threat Mitigation in Healthcare and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(Special Issue 1), 5-12.
26. Rahman, M. B., Yasin, M., & Ahmed, M. P. (2024). Data-Driven Population Health Analytics for Identifying High-Risk Groups and Health Disparities. American Journal Of Botany And Bioengineering, 1(11), 58-82.
27. Mallireddy, S. (2024). Servicenow Create Enterprise Workflows for Various Digitalize Business Processes. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(4), 1-6.
28. Karvannan, R. (2023). Empowering healthcare operations with next-generation compliance and inventory solutions. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 297–313.
29. Hossain, M. S., Ali, M., & HOSSAIN, M. S. (2023). AI-Enhanced Labor Market Analytics to Predict Workforce Shifts and Support Policy Decisions in the US Economy. Journal of Computer Science and Technology Studies, 5(1), 101-120.
30. Sugumar, R. (2024). Next-generation security operations center (SOC) resilience: Autonomous detection and adaptive incident response using cognitive AI agents. International Journal of Technology, Management and Humanities, 10(02), 62-76.
31. Vankayala, S. C. (2023). Governed Autonomy in Reliability Engineering: Integrating Error Budgets with AI-Driven Remediation. J Artif Intell Mach Learn & Data Sci 2023, 1(2), 3191-3196.
32. Bonthala, D. (2025). Telemetry Driven Cost Governance for Enterprise Data and AI Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(1), 9361-9372.
33. Gopinathan, V. R. (2024). Secure explainable AI on Databricks–SAP cloud for risk-sensitive healthcare analytics and swarm-based QoS control. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8452-8459.
34. Yamsani, N. (2016). Designing enterprise-wide reference data foundations for consistency, control, and operational integrity across complex institutional environments. International Journal of Scientific Research & Engineering Trends, 2(5). https://doi.org/10.5281/zenodo.18296676
35. Suvvari, S. K. (2023). Shift Left: Moving the Inclusion of Accessibility Functionalities to the Left in Agile Product Development Life Cycle. Journal of Computational Analysis and Applications, 31(4).
36. Nallamothu, T. K. (2025, November). Next-Generation Clinical Documentation: Ambient AI and Automated Workflows with DAX Copilot. In 2025 5th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 986-990). IEEE.
37. Kasireddy, J. R. (2025). The ethical implications of AI in financial market surveillance: Are we over-monitoring traders? European Journal of Accounting, Auditing and Finance Research, 13(4), 17–36. https://doi.org/10.37745/ejaafr.2013/vol13n41736
38. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
39. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
40. Dave, B. L. (2024). Driving Salesforce Testing Excellence with AI and Metadata-Driven Intelligent Automation. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10647-10655.
41. Mali, R. K. (2023). A Scalable Microservice Framework for Multi-Modal Logistics Route Optimization. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(2), 8382-8391.
42. Narayanan, S. (2024). Third-party AI vendor risk: Developing assessment frameworks for machine learning service providers. International Journal of Computer Science and Engineering and Information Technology, 10(4), 1133–1142. https://philarchive.org/archive/NARTAV
43. Vankayala, S. C. (2021). Engineering Quality into Cloud-Native Financial Platforms on Microsoft Azure. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(1), 4361-4367.
44. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
45. Prasad, P. K. (2025). Federated Agentic SRE—Cross-Vendor, PrivacyPreserving Agent Federations for Hybrid Multi-Cloud Incident Response. Journal of Computational Analysis & Applications, 34(11).
46. Parupalli, A., & Pandya, S. (2022). Compliance-Driven Data Governance: A Survey on GDPR, and HIPAA in Cloud Databases. vol, 12, 828-836.
47. Mathew, A., Jackson, E., & Tobesman, A. (2025). Agentic AI: A Game-Changer in Cybersecurity Defense. Science and Technology: Developments and Applications Vol. 7, 112-120.
48. Balamuralidhar Sarabu, V. (2021). System-of-record governance in enterprise retail platforms: Architectural design principles for financial data ownership and consistency. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(2), 1–16.