A Secure DevSecOps-Driven Cloud Intelligence Framework: Integrating Deep Neural Networks, Distributed Systems, and NLP for AI-First Banking
DOI:
https://doi.org/10.15662/IJEETR.2022.0406010Keywords:
DevSecOps, Cloud Security, AI-First Banking, Deep Neural Networks, Distributed Systems, NLP, Data Mining, Cybersecurity Automation, Azure DevOps, GitHub Actions, Fraud Detection, Threat Intelligence, Microservices, Real-Time Analytics, Secure CI/CDAbstract
AI-first banking demands intelligent, scalable, and secure architectures capable of defending against rapidly evolving cyber threats while enabling real-time analytics and automated decision-making. This paper proposes a Secure DevSecOps-Driven Cloud Intelligence Framework that integrates Deep Neural Networks (DNNs), distributed cloud systems, Natural Language Processing (NLP), and continuous security automation to enhance security, resilience, and operational efficiency in modern banking ecosystems.
The framework embeds security controls across the development pipeline using DevSecOps principles and leverages Azure DevOps and GitHub automation for continuous integration, delivery, and monitoring. A distributed DNN architecture is utilized for anomaly detection, fraud prediction, and risk scoring, while NLP-driven threat mining extracts actionable insights from logs, user interactions, and financial documents. The system operates on an elastic cloud environment, enabling auto-scaling, microservices orchestration, and real-time data mining across high-volume banking workloads.
Experimental results demonstrate improved security posture through automated policy enforcement, faster threat detection via deep learning, and reduced operational overhead due to continuous monitoring and intelligent feedback loops. This unified framework provides a robust foundation for secure, intelligent, and scalable AI-first banking, addressing critical challenges in cybersecurity, data governance, continuous compliance, and distributed system reliability.
References
1. Weizenbaum, J. (1976). Computer Power and Human Reason: From Judgment to Calculation. W. H. Freeman & Company. Wikipedia
2. Sugumar, R., & Rajesh, A. (2019). Performance analysis and evaluation of multi-cloud systems. i-manager's Journal on Cloud Computing, 6(1), 36.
3. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
4. Gu¬pta, A. (2019). The Ethics of AI in Finance. (Montreal Ethics AI). Montreal AI Ethics Institute
5. Mohile, A. (2021). Performance Optimization in Global Content Delivery Networks using Intelligent Caching and Routing Algorithms. International Journal of Research and Applied Innovations, 4(2), 4904-4912.
6. (2020). The ethics of artificial intelligence: Issues and initiatives. European Parliament
7. TCS. (2021). A Catalyst for Digital Transformation of the Banking Industry (White Paper). Tata Consultancy Services
8. 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. https://doi.org/10.15680/IJCTECE.2022.0502003
9. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
10. Sudha, N., Kumar, S. S., Rengarajan, A., & Rao, K. B. (2021). Scrum Based Scaling Using Agile Method to Test Software Projects Using Artificial Neural Networks for Block Chain. Annals of the Romanian Society for Cell Biology, 25(4), 3711-3727.
11. Kotapati, V. B. R., Pachyappan, R., & Mani, K. (2021). Optimizing Serverless Deployment Pipelines with Azure DevOps and GitHub: A Model-Driven Approach. Newark Journal of Human-Centric AI and Robotics Interaction, 1, 71-107.
12. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.
13. Chatterjee, P. (2019). Enterprise Data Lakes for Credit Risk Analytics: An Intelligent Framework for Financial Institutions. Asian Journal of Computer Science Engineering, 4(3), 1-12. https://www.researchgate.net/profile/Pushpalika-Chatterjee/publication/397496748_Enterprise_Data_Lakes_for_Credit_Risk_Analytics_An_Intelligent_Framework_for_Financial_Institutions/links/69133ebec900be105cc0ce55/Enterprise-Data-Lakes-for-Credit-Risk-Analytics-An-Intelligent-Framework-for-Financial-Institutions.pdf
14. Ravipudi, S., Thangavelu, K., & Ramalingam, S. (2021). Automating Enterprise Security: Integrating DevSecOps into CI/CD Pipelines. American Journal of Data Science and Artificial Intelligence Innovations, 1, 31-68.
15. 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.
16. Selvi, R., Saravan Kumar, S., & Suresh, A. (2014). An intelligent intrusion detection system using average manhattan distance-based decision tree. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 205-212). New Delhi: Springer India.
17. Innowise. (2021). DevOps in Banking Sector – DevOps Toolkit Implementation.
