Dynamic Configuration Assurance for Enterprise Cloud Platforms Using Infrastructure-as-Code and Drift Detection
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Abstract
Enterprise cloud platforms require continuous configuration assurance to maintain security, compliance, operational consistency, and service reliability across dynamic computing environments. The increasing adoption of multi-cloud architectures, containerized applications, and automated deployment pipelines has introduced challenges in maintaining consistency between approved infrastructure configurations and actual deployed resources. Manual configuration management and periodic auditing are often insufficient to identify unauthorized modifications, configuration inconsistencies, and security policy violations promptly. This study examines dynamic configuration assurance through the integration of Infrastructure-as-Code (IaC), automated configuration validation, and continuous drift detection. The proposed approach establishes version-controlled configuration baselines, validates infrastructure changes before deployment, and continuously compares live cloud resources against their intended state. Configuration differences are classified according to their severity, operational impact, security implications, and compliance requirements. Automated remediation mechanisms are incorporated to correct approved deviations, while potentially disruptive changes are escalated for human review. The research methodology adopts a design-oriented approach supported by controlled cloud experiments and quantitative performance evaluation. Key evaluation metrics include drift detection accuracy, detection latency, remediation success rate, configuration compliance, and false-positive frequency. The study also considers policy enforcement, auditability, change traceability, and rollback capabilities as essential components of reliable configuration assurance. The proposed framework aims to strengthen enterprise cloud governance by integrating preventive validation with continuous monitoring and controlled remediation, thereby reducing configuration-related risks and improving infrastructure consistency across distributed cloud environments.
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1. He, H., Li, X., Chen, P., Chen, J., Liu, M., et al. (2024). Efficiently localizing system anomalies for cloud infrastructures: A novel Dynamic Graph Transformer based Parallel Framework. Journal of Cloud Computing, 13, Article 115. https://doi.org/10.1186/s13677-024-00677-x
2. Rengarajan, A. (2022). Developing AI Powered Enterprise Integration Platforms Through Cloud Native Observability and Automated Governance. International Journal of Science, Research and Technology, 5(6), 8970–8977.
3. Kalakoti, M. K. R. (2023). Interference-aware resource scheduling for cloud microservices on Kubernetes. International Journal of Future Innovative Science and Technology, 6(1), 9900–9909.
4. Garg, D. K. (2025). Manufacturing execution systems: A comprehensive guide to selection and implementation. International Journal of Research in Computer Applications and Information Technology, 8(1), 2001–2015. https://doi.org/10.34218/IJRCAIT_08_01_146
5. Padmanabham, S. (2023). Secure API gateway architecture for open banking. International Journal of Computer Technology and Electronics Communication, 6(6), 8166–8174.
6. Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3–15.
7. Gopinathan, V. R., & Mali, R. K. (2022). Building cognitive technology frameworks through artificial intelligence SAP cloud automation and enterprise intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7152–7162.
8. Katakam, R. (2023). Automated loan processing and payment reconciliation through integrated banking workflows. International Journal of Science, Research and Technology, 6(5), 10733–10744.
9. Neela, S. (2024). Secure and zero-trust middleware architectures for real-time enterprise data exchange. International Journal of AI, BigData, Computational and Management Studies, 5(4), 220–227.
10. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533–5537.
11. Mysula, S. (2025). Engineering resilient enterprise integration platforms through automated monitoring and intelligent error management. International Journal of Computer Technology and Electronics Communication, 8(2), 10508–10513.
12. Vemireddy, S. (2023). Engineering regulatory-aware data processing systems for tax advisory platforms. International Journal of Science, Research and Technology, 6(1), 9243–9249.
13. Ahuja, D. (2023). Multi-Cloud Management Strategies: Tools, Challenges, and Implementation Methods. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, 9(10), 493–506.
14. Karrothu, A. (2025). Exploitability-aware vulnerability prioritization for automated security enforcement in cloud-native applications. International Journal of Science, Research and Technology, 8(2), 13948–13961.
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. Chinthamani, R. (2023). Engineering resilient microservices for real-time retail order and inventory synchronization. International Journal of Engineering & Extended Technologies Research, 5(6), 7774–7779.
17. Gunasekaran, R. M. N. (2024). AI/ML and Generative AI Use Cases in Financial Services. The Eastasouth Journal of Information System and Computer Science, 2(02), 345–356.
18. Mudunuri, P. R. (2024). Operational transparency as a compliance mechanism in federal DevOps ecosystems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(3), 8131–8142.
19. Tarigoppula, S. (2021). Policy-driven autonomous infrastructure provisioning: A framework for enterprise cloud governance using infrastructure as code. International Journal of Emerging Trends in Engineering and Management Research, 6(5), 10361–10369.
20. Bandari, U., & Jinna, K. R. (2024). LLM-assisted anonymization of customer experience data: A comparative study of rule-based, NER, and generative approaches. International Journal of Research and Applied Innovations, 7(1), 10164–10174.
21. Kopparthi, G. S. (2023). Trustworthy artificial intelligence for high-stakes decision-making: Explainability, human oversight, and system reliability. International Research Journal of Innovative Engineering, 7(4), 12847–12860.
22. Polamarasetty, V. K. (2024). Workday-based enterprise benefits management: Configuration, automation, and operational optimization. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(5), 11347–11352.
23. Gantikota, S. (2023). Reducing HL7 Processing Errors through Automated File Creation and Ingestion Pipelines: A Production Case Study in EHR Data Integration. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 241–245.
24. Selvarajan, K. (2022). Architecting scalable self-service data platforms for enterprise analytics. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7427–7436.
25. Basavala, S. R. (2025). Continuous application security posture management through AI-driven risk correlation and vulnerability prioritization. International Journal of Research Publications in Engineering, Technology and Management, 8(3), 12175–12187.
26. Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231–244). Singapore: Springer Nature Singapore.
27. Karuturi, Y. (2025). Serverless computing framework for enterprise ERP background job processing in cloud SAP environments. International Journal of Science, Research and Technology, 8(2), 13938–13947.
28. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
29. Yadavalli, V. R. (2025). Incident memory graphs for predictive AMS triage and self-improving SAP operations. International Journal of Science, Research and Technology, 8(2), 13913–13927.
30. Kalabhavi, V. (2024). Promotional spend analytics using AI for PFME allocation and coupon redemption optimization. International Journal of Emerging Trends in Engineering and Management Research, 9(2), 15406.
31. Ramasamy, M. (2022). Architecting scalable intent-based networking platforms for enterprise automation. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(3), 11812–11823.
32. Madhan, E. S., Kannan, K. S., Rani, P. S., Rani, J. V., & Anguraj, D. K. (2021). A distributed submerged object detection and classification enhancement with deep learning. Distrib. Parallel Databases, 41, 1–17.
33. Natta, P. K. (2025). AI-driven decision intelligence: Optimizing enterprise strategy with AI-augmented insights. Journal of Computer Science and Technology Studies, 7(2), 146–152.
34. Chandran, A. S. (2023). A unified Spark ETL stack across Cloudera CDP, Azure HDInsight, and Databricks. International Journal of Future Innovative Science and Technology, 6(6), 11769–11779.
35. Jinna, K. R., & Thilagaraj, S. V. (2024). Tracing across the seam: OpenTelemetry-based observability for hybrid estates where message-oriented middleware and Kubernetes microservices coexist. International Research Journal of Innovative Engineering, 8(5), 15438–15448.
36. Shaik, N. (2024). Right-sizing at scale: A data-driven approach to Kubernetes cost optimization. International Journal of Future Innovative Science and Technology, 7(1), 12068–12078.
37. Gadige, C. D. (2024). Enterprise integration architecture for AI-powered CRM: Combining Salesforce, MuleSoft, data platforms, and autonomous agents. International Journal of Research and Applied Innovations, 7(3), 10850–10860.
38. Gurram, S. (2024). The End of Generative AI Experiments Designing Production‑Grade Data Architectures for LLM Systems. International Journal of Computer Technology and Electronics Communication, 7(1), 8233-8242.