Cognitive Intelligence Frameworks for High-Performance Cloud Systems and Secure Data-Centric Computing
Abstract
Cognitive intelligence frameworks are emerging as a transformative approach for enhancing the performance, adaptability, and security of modern cloud computing systems. These frameworks integrate artificial intelligence, machine learning, and advanced data analytics to enable intelligent decision-making, self-optimization, and autonomous system management. In high-performance cloud environments, cognitive intelligence supports efficient resource allocation, workload balancing, and predictive maintenance, thereby improving system reliability and scalability. Simultaneously, secure data-centric computing emphasizes the protection of data throughout its lifecycle, including storage, processing, and transmission. By incorporating cognitive techniques such as anomaly detection, behavioral analysis, and real-time threat intelligence, these frameworks strengthen data security and mitigate risks associated with cyber threats. The convergence of cognitive intelligence and cloud computing also facilitates the development of adaptive security models that respond dynamically to evolving threats. However, challenges such as data privacy concerns, computational complexity, and integration issues persist. This study explores the architecture, technologies, and methodologies underlying cognitive intelligence frameworks in cloud systems, highlighting their role in achieving high performance and secure data management. The findings demonstrate the potential of these frameworks to revolutionize cloud computing by enabling intelligent, secure, and efficient data-driven environments.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 9 No. 2 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
402-410 |
Published |
April 18, 2026 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Subramanian Ramamoorthy (2026). Cognitive Intelligence Frameworks for High-Performance Cloud Systems and Secure Data-Centric Computing. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 9 No. 2 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 402-410. https://doi.org/10.15662/IJAESIT.2026.0902002 |
References
2. Ganesh, N., & Srinivasa Rao, T. (2025). Advancing sustainability in cloud computing: energy-efficient resource allocation and green infrastructure strategies. Advancing Sustainability in Cloud Computing: Energy-Efficient Resource Allocation and Green Infrastructure Strategies.
3. Barigidad, S., Hameed, S., Karri, N., Jangam, S. K., Pedda, P. S. R., & Gupta, D. (2025, December). Computational Modeling of AI-Enhanced Learning Pathways: A Mathematical Framework for Optimizing Knowledge Acquisition, Cognitive Load Management, and Student Performance in STEM Education. In 2025 International Conference on AI-Driven STEM Education and Learning Technologies (AISTEMEDU) (pp. 1-7). IEEE.
4. Mudunuri, P. R. (2023). Governance-Aware Infrastructure-as-Code for Regulated Research Environments. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9017-9027.
5. Singh, A. (2021). Unlocking Mesh Networks: Tackling Scalability in Dynamic Environments. IJSAT-International Journal on Science and Technology, 12(1).
6. Kunadi, S. K. (2026). AI-Driven Data Enrichment and Golden Record Creation for Enterprise Customer Data Platforms. International Journal of Research and Applied Innovations, 9(1), 13630-13640.
7. Cherukuri, B. R. (2024, February). Development of Design Patterns with Adaptive User Interface for Cloud Native Microservice Architecture Using Deep Learning With IoT. In 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) (Vol. 5, pp. 1866-1871). IEEE.
8. Tohfa, N. A., Hossen, S., Rahman, R., Bashir, T., Mondal, P., Zareen, S., ... & Faizul, A. (2026, February). Predicting Heart Disease Using Machine Learning and Ensemble Models: A Comparative Study. In 23 RD INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS.
9. Anbazhagan, K. (2025). Next-Generation Enterprise Cloud AI for Healthcare: Secure CNN Pipelines and Privacy Controls. International Journal of Future Innovative Science and Technology (IJFIST), 8(6), 15980.
10. Gentyala, R. (2023). Anticipating Clinical Decay: A Meta-Learning Framework for Proactive Drift Detection and Feature Attribution in Deployed Healthcare AI. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 198-216.
11. ALAM, M. A., Alam, M. K., & Mahmud, M. A. (2025). Deep Learning for Early Detection of Systemic Risk in Interconnected Financial Markets: A US Regulatory Perspective. Journal of Computer Science and Technology Studies, 7(9), 353-375.
12. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
13. 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.
14. Niture, N., & Abdellatif, I. (2025). A systematic review of factors, data sources, and prediction techniques for earlier prediction of traffic collision using AI and machine learning. Multimedia Tools and Applications, 84(18), 19009-19037.
15. Gurram, S. (2025). Adaptive Drift Defense: A Unified Framework for Data, Task, And User-Intent Drift in LLM Apps. International Journal of Research and Applied Innovations, 8(6), 3721-3729.
16. Sahid, M. H., Pratama, D. A., Abd Rahman, M., Vardhani, A. K., Kulsum, D. U., Tanaka, J., ... & Renaldi, T. (2026). Kesehatan Masyarakat Di Era Digital. CV Eureka Media Aksara.
17. Pradhan, C., & Trehan, A. (2025). Integration of blockchain technology in secure data engineering workflows. International Journal of Computer Sciences and Engineering, 13(1), 01-07.
18. Patel, P., & Chaturvedi, V. (2022). Development of an AI-Based Adaptive Control System for Real-Time HVAC Performance Enhancement. International Journal of Engineering Science & Humanities, 12(2), 41-52.
19. Padala, S. (2021). Cloud-Enabled AI Contact Centers in Oncology Care. International Journal of AI, BigData, Computational and Management Studies, 2(3), 93-98.
20. Jabed, M. M. I., Ferdous, S., Ankhi, R. B., Gupta, A. B., & Hossain, M. S. (2025). AI-Driven Intrusion Detection Systems: A Business Analyst’s Framework for Enhancing Enterprise Security and Intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(5), 12708-12719.
21. Md Shahadat Hossain, M. S. H., Md Shahdat Hossain, M. S. H., Mohammad Ali, M. A., & Md Whahidur Rahman, M. W. R. (2025). Machine Learning-Based Analytics Framework for Detecting Tax Evasion and Financial Misconduct in US Enterprises. Machine Learning-Based Analytics Framework for Detecting Tax Evasion and Financial Misconduct in US Enterprises, 2(12), 114-138.
22. Mathew, A. Trust Is Not a Default Control: AI-Powered Social Engineering and the Need to Have New Governance.
23. Boddupally, H. L. (2022). Toward self-optimizing enterprise applications: AI-guided profiling and performance optimization for C# and SQL-based systems. SSRN. https://doi.org/10.2139/ssrn.6270498
24. Hasib, A., Akib, A. S. M., Ankur, N. D., & Giri, A. (2026). Dual-Modality IoT Framework for Integrated Access Control and Environmental Safety Monitoring with Real-Time Cloud Analytics. arXiv preprint arXiv:2601.20366.
25. Sugumar, R. (2025). Cyber-Secure Cloud Architecture Integrating Network and API Controls for Risk-Aware SAP Healthcare Data Platforms. International Journal of Humanities and Information Technology, 7(4), 53-60.
26. Sundaresh, G., Ramesh, S., Malarvizhi, K., & Nagarajan, C. (2025, April). Artificial Intelligence Based Smart Water Quality Monitoring System with Electrocoagulation Technique. In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (pp. 1-6). IEEE.
27. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
28. Karvannan, R. (2025). Scalable cloud architecture for synchronizing pharmacy inventory between central and local systems. International Journal of Information Technology, 6(1), 118–131. https://doi.org/10.34218/IJIT_06_01_011
29. Barve, P. S., Vigenesh, M., Deshpande, V., Wanjari, M. B., & Patil, S. (2023, December). A Non-Linear Dimensionality Reduction Approach for Unmixing Hyper Spectral Data. In 2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC) (pp. 1718-1724). IEEE.
30. Beeram, S. (2026). AI-Augmented DevSecOps in Azure Pipelines. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 7(1), 46-48.
31. Gollapudi, R. (2025). Telemetry-Driven Predictive Failure Models for High-Scale Financial Databases. Journal of Computational Analysis and Applications, 34(12).
32. Parasa, M. (2021). Encryption-aware data integrity and quality controls in SAP SuccessFactors integrations using machine learning and cryptographic hash chains for tamper detection. International Journal of Computer Technology and Electronics Communication, 4(6), 4304–4316. https://doi.org/10.15680/IJCTECE.2021.0406014
33. Devineni, A. (2025). Post-Mortem Intelligence: Using Large Language Models to Build Proactive Reliability Knowledge Graphs from Incident Documentation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(3), 170-175.
34. Subramanyam, S. P. (2025, December). Enterprise Data Modernization: A Case Study on NoSQL Migration and ETL Optimization Using Azure Cosmos DB. In 2025 16th International Conference on Software Engineering and Service Science (ICSESS) (pp. 1-6). IEEE.
35. Namdeo, A. (2024). Autonomous data quality management via ML in cloud warehouses. International Journal of Humanities and Information Technology, 6(4), 124–125. https://doi.org/10.21590/ijhit.06.04.14
36. Karnam, V. S. (2025). Intelligent SOS (Safety and Security operations): Real-Time Surveillance with Risk Forecasting and Assessment of SOS (Safety and Security operations) using Edge-AI and Cloud Infrastructure. Journal Of Multidisciplinary, 5(7), 552-562.
37. Pothuri, M. K. Building a Seamless Healthcare Data Fabric: Zero-Touch Integration and Scalable Mapping Across Provider, Claims, Recipient, and Pharmacy Source Systems for State Medicaid. IJLRP-International Journal of Leading Research Publication, 6(8).
38. Panyala, V. R. (2023). AI-augmented DevOps frameworks for accelerating cloud-native platform engineering at scale. International Journal of Research and Applied Innovations, 6(1), 8375–8379.
39. Lakshmi Prasad Rongali. (2025). Integrating AI and Devops Practices to Develop Cybersecurity Frameworks That Enhance Resilience in Utility Infrastructure. Journal of Informatics Education and Research, 5(2). https://doi.org/10.52783/jier.v5i2.2838
40. Pasumarthi, H. (2026). From monolith to microservices: Redesigning financial data systems for resilience and scalability. International Journal of Engineering & Extended Technologies Research, 8(1), 194–197.