Building Secure Enterprise Intelligence using AI and Cloud Computing for Scalable Digital Systems
DOI:
https://doi.org/10.15662/IJARCST.2023.0605014Keywords:
Enterprise Intelligence, Artificial Intelligence, Cloud Computing, Data Security, Scalability, Machine Learning, Cybersecurity, Big Data Analytics, Digital Transformation, Secure ArchitectureAbstract
The rapid evolution of digital technologies has transformed how enterprises manage, process, and utilize data for decision-making. Enterprise Intelligence (EI), powered by Artificial Intelligence (AI) and Cloud Computing, enables organizations to derive actionable insights, enhance operational efficiency, and achieve scalability. However, with increased data reliance comes heightened concerns around security, privacy, and system resilience. This paper explores the integration of AI-driven analytics with cloud-based infrastructures to build secure and scalable enterprise intelligence systems. It emphasizes the role of advanced machine learning algorithms, distributed computing, and secure cloud architectures in enabling real-time decision-making. The study also highlights key security challenges such as data breaches, unauthorized access, and compliance issues, proposing mitigation strategies including encryption, zero-trust architecture, and AI-based threat detection. Furthermore, it examines how scalable cloud platforms facilitate elasticity and cost efficiency while supporting large-scale data processing. By combining security frameworks with intelligent automation, enterprises can create robust digital ecosystems capable of adapting to dynamic business environments. This research provides insights into designing secure, scalable EI systems and outlines best practices for organizations seeking to leverage AI and cloud technologies effectively.
References
1. Sanepalli Uttama Reddy (2023). Cognitive goal-driven financial infrastructure A cloud-native AI-orchestrated architecture for investment trade settlement and risk management systems. World Journal of Advanced Research and Reviews 19(1) 1659–1667.
2. Vayyasi, N. K. (2023). Optimizing factory maintenance and downtime prediction through Java-driven AI pipelines. International Journal of Research and Applied Innovations (IJRAI), 6(3).
3. Gurram S. (2023). Why Data Engineering Not Model Scale Became the True Bottleneck in Generative AI. International Journal of Research Publications in Engineering Technology and Management (IJRPETM) 6(4) 9028-9036.
4. Kumar S. S. (2023). AI-Based Data Analytics for Financial Risk Governance and Integrity-Assured Cybersecurity in Cloud-Based Healthcare. International Journal of Humanities and Information Technology 5(04) 96-102.
5. Anand L. (2023). An Intelligent AI and ML–Driven Cloud Security Framework for Financial Workflows and Wastewater Analytics. International Journal of Humanities and Information Technology 5(02) 87-94.
6. Ramakrishna S. (2023). Cloud-Native AI Platform for Real-Time Resource Optimization in Governance-Driven Project and Network Operations. International Journal of Engineering & Extended Technologies Research (IJEETR) 5(2) 6282-6291.
7. Gentyala R. (2022). A Hybrid Machine Learning Approach for Credit Scoring Integrating Traditional Financial History with Mobile Phone Behavioral Metrics. International Journal of Artificial Intelligence and Machine Learning Research and Development (QITP-IJAIMLRD) 3(1) 13-40.
8. Appani C. and Guda D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security 2023(7) 20–31.
9. Vankayala S. C. (2021). Designing an Advanced Quality Assurance Framework to Ensure Accuracy Regulatory Compliance and Operational Reliability across End-to-End Mortgage Origination and Underwriting Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR) 3(6) 4034-4044.
10. Madhava Rao Thota (2019). Policy-Driven Automation for Scalable Governance in Enterprise Big Data Platforms. International Journal of Scientific Research & Engineering Trends 5(6).
11. Begum R. S. and Sugumar R. (2016). Conditional entropy with swarm optimization approach for privacy preservation of datasets in cloud. Indian Journal of Science and Technology 9(28).
12. Nagarajan G. (2022). Optimizing project resource allocation through a caching-enhanced cloud AI decision support system. International Journal of Computer Technology and Electronics Communication 5(2) 4812–4820.
13. Hebbar K. S. (2022). Machine learning-assisted service boundary detection for modularizing legacy systems. International Journal of Applied Engineering & Technology 4(2) 401–414.
14. Sarabhu V. B. and Balaji V. (2018). Advanced memory virtualization technique for efficient access of data resources in cloud environment. International Journal of Research Publications in Engineering Technology and Management (IJRPETM) 1(3) 623–629.
15. Hossain I. Tohfa N. A. Zareen S. Rahman M. Rasul I. and Shakhawat M. (2022). Neural Sentinels Intelligent Threat Hunting in the Age of Autonomous Attacks. World Journal of Advanced Research and Reviews 16(03) 1480-1488.
16. Ghanta S. (2021). A system-level approach to intelligent root cause discovery in distributed Java microservices. International Journal of Science Engineering and Technology.
17. Parepalli S. (2021). Mapping Critical Data Relationships to Enable Automated Evaluation of Operational Impact. J Artif Intell Mach Learn & Data Sci 1(1) 3175-3184.
18. Agarwal S. (2022). Observability in Microservices From Traditional Monitoring to Distributed System Intelligence. International Journal of Computer Technology and Electronics Communication 5(6) 16220-16226.
19. Ranjith Rajasekharan (2019). Hybrid cloud architecture for enterprise database system. International Journal of Science Research and Technology (IJSRAT) 2(6).
20. Niture N. A. and Abdellatif I. (2020). AI based airplane air pollution identification architecture using satellite imagery. IEEE Cloud Summit pp 150-155.
21. Patel P. and 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.
22. Meka S. (2022). Engineering Insurance Portals of the Future Modernizing Core Systems for Performance and Scalability. International Journal of Computer Science and Information Technology Research 3(1) 180-198.
23. Garg V. K. Soundappan S. J. and Kaur E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology 2(6) 62–64.
24. Sudhan S. K. H. H. and Kumar S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology 8(35) 1-5.
25. Jayaraman S. Rajendran S. and 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.
26. Jagadeesh S. and Sugumar R. (2017). A comparative study on artificial bee colony with modified ABC algorithm. European Journal of Applied Sciences 9(5) 243-248.
27. Thumala Srinivasarao (2020). Building highly resilient architectures in the cloud. Nanotechnology Perceptions 16(2).
28. Devarajan R. Prabakaran N. Vinod Kumar D. Umasankar P. Venkatesh R. and Shyamalagowri M. (2023). IoT based underground cable fault detection with cloud storage. IEEE ICAISS pp 1580-1583.
29. Swetha M. S. and Sarraf G. (2019). Spam email and malware elimination employing various classification techniques. IEEE RTEICT pp 140-145.
30. Potel R. (2021). A Data-Driven Architecture for Preemptive Cyber Defense Using AI-Based Governance and Autonomous Remediation. International Journal of Engineering & Extended Technologies Research (IJEETR) 3(6).
31. Vimal Raja G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering 9(12) 14705-14710.
32. Anand L. and Syed Ibrahim S. P. (2018). HANN a hybrid model for liver syndrome classification by feature assortment optimization. Journal of Medical Systems 42(11) 211.
33. Mudunuri P. R. (2023). Automation-Driven Reliability Engineering for Public-Sector Biomedical Systems. International Journal of Humanities and Information Technology 5(01) 68-86.
34. Kabade S. and Sharma A. (2022). Utilizing cloud technologies to reduce bottlenecks in retirement claim approvals for scalable and efficient processing. International Journal of Current Science 12(3).
35. Vimal Raja G. (2022). Leveraging machine learning for real-time short-term snowfall forecasting using multisource atmospheric and terrain data integration. International Journal of Multidisciplinary Research in Science Engineering and Technology 5(8) 1336-1339.
36. Padala S. (2022). Omnichannel AI-Enabled Healthcare Contact Centers Enabling Seamless Patient Journey Continuity. International Journal of AI BigData Computational and Management Studies 3(1) 133-139.
37. 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
38. Ireddy R. K. (2023). API-driven interoperability framework for corporate treasury management A financial data exchange standard implementation with secure data aggregation networks. World Journal of Advanced Research and Reviews 19(2) 1727-1738.
39. Kothokatta L. (2025). Building Resilient CI CD Pipelines for OTT Workloads Using Quality Gates. ISCSITR International Journal of Computer Science and Engineering (IJCSE) 6(4) 29-45.
40. Sheta S. V. (2023). The importance of software documentation in the development and maintenance phases. REDVET Revista Electronica de Veterinaria 24(3) 609–618.
