Autonomous AI Governance and Cost Optimization Strategies for Multi-Tenant Enterprise and Data Platforms
DOI:
https://doi.org/10.15662/IJARCST.2026.0902008Keywords:
Autonomous AI, AI governance, cost optimization, multi-tenant platforms, enterprise data platforms, FinOps, cloud computing, resource orchestration, predictive scaling, explainable AI, AI lifecycle management, workload optimization, data governance, cloud efficiency, intelligent automationAbstract
The rapid adoption of autonomous artificial intelligence (AI) in enterprise ecosystems has transformed the operational dynamics of multi-tenant enterprise and data platforms. These platforms support multiple organizations, business units, and applications on shared infrastructure, enabling scalability, efficiency, and resource pooling. However, the increasing autonomy of AI systems introduces complex challenges in governance, compliance, transparency, security, and cost management. Autonomous AI systems dynamically manage workloads, allocate computing resources, and optimize operations, but without strong governance frameworks, they may lead to inefficiencies, regulatory violations, and financial overruns.
This paper examines autonomous AI governance and cost optimization strategies designed for multi-tenant enterprise environments. It explores how AI lifecycle governance, policy enforcement, explainable AI (XAI), and compliance automation can ensure responsible AI operations. Additionally, it investigates cost optimization mechanisms including predictive scaling, intelligent workload scheduling, FinOps integration, serverless architectures, and resource utilization analytics. The study emphasizes the integration of governance and financial optimization as a unified framework rather than separate operational concerns.
A qualitative and conceptual research methodology is employed, drawing insights from academic literature, industry reports, and enterprise best practices. The findings highlight that organizations implementing integrated governance and cost optimization frameworks achieve improved operational efficiency, reduced infrastructure costs, enhanced regulatory compliance, and greater system resilience in multi-tenant AI-driven environments.
References
1. Amazon Web Services. (2023). Cloud financial management and FinOps best practices. AWS Publications.
2. Bhimani, A. (2021). Digital data and management accounting: Why we need to rethink research methods. Journal of Management Control, 32(1), 9–23.
3. Juvvadi, R. R. (2022). Machine learning for anomaly detection in the financial close: A journal entry risk-scoring framework for SAP S/4HANA. International Journal of Communication Networks and Information Security, 14(3), 1684–1695.
4. Khan, M. I. (2025). Managing threats in cloud computing: A cybersecurity risk mitigation framework. International Journal of Advanced Research in Computer Science, 15(5).
5. Joyce, S., Anbalagan, B., Pasumarthi, A., & Bussu, V. R. R. (2025). Platform reliability in Microsoft Azure: Architecture patterns and fault tolerance for enterprise workloads. International Journal of Information Technology and Management Information Systems, 16(4), 1–19. https://doi.org/10.34218/IJITMIS_16_04_001
6. Islam, M. S., Tohfa, R. I., & Hasan, M. M. (2026). Generative AI Adoption and Industry-Level Productivity Growth in the United States: A Multi-Sector Empirical Analysis. American Journal of Economics and Business Management, 9(4), 594-613.
7. Rongali, L. P. (2025). DevSecOps for Critical Energy Infrastructure: A Secure and Sustainable Paradigm. https://doi.org/10.36227/techrxiv.175433224.4 9519285/v1
8. European Union. (2018). General Data Protection Regulation (GDPR). Official Journal of the European Union.
9. FinOps Foundation. (2023). FinOps framework: Cloud financial management. FinOps Foundation Publications.
10. Gartner. (2022). Emerging technologies for autonomous enterprise governance. Gartner Research Reports.
11. Navandar, P. (2024). Governance, risk, and compliance (GRC) in the age of identity and access governance (IAG): A framework for integrated enterprise security and compliance. International Journal of Research and Applied Innovations (IJRAI), 7(2), 10483–10493. https://doi.org/10.15662/IJRAI.2024.0702011
12. ISO. (2022). ISO/IEC 27001: Information security management systems. International Organization for Standardization.
13. Khan, L. U., Saad, W., Han, Z., Debbah, M., & Hong, C. S. (2021). Federated learning for internet of things: Recent advances, taxonomy, and open challenges. IEEE Communications Surveys & Tutorials, 23(3), 1759–1799.
14. Kumar, K., & Singh, R. (2022). AI-driven governance and optimization in cloud-native enterprise systems. International Journal of Cloud Applications and Computing, 12(4), 45–63.
15. Damarched, M. K. (2025). Data Governance Challenges in ITSM Platform Transitions. International Journal of Computer Technology and Electronics Communication, 8(6), 11881-11890.
16. Suddala, V. R. A. K. (2025). Healthcare e-commerce platforms driving secure, scalable, and auditable service delivery. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(1), 9340–9351.
17. Yatam, S. N. K. (2025). Autonomous DevOps: The ZTI-MDS Integration Framework. Journal of Computer Science and Technology Studies, 7(7), 755-763.
18. Anumula, S. K., & Tatavarthy, K. (2025, July). Balancing Innovation and Ethics: Navigating the Promise and Perils of Algorithmic Solutions in Humanitarian Innovation. In Networking International Conference on Emerging Trends in Expert Applications and Security (pp. 308-319). Cham: Springer Nature Switzerland.
19. Gopisetty, S. (2025). The Babelfish for cloud policies: Using AI to harmonize zero-trust rules across banking microservices. International Journal of Artificial Intelligence and Cloud Computing, 3(2), 1–17. https://doi.org/10.34218/IJAICC_03_02_001
20. Polamreddy, V. R. (2025). Architecting Financially Compliant Enterprise Point-of-Sale Systems: Data Integrity and Revenue Recognition at Scale. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(5), 12993-13104.
21. Manda, P. (2025). Disaster recovery by design: Building resilient Oracle database systems in cloud and hyperconverged environments. International Journal of Research and Applied Innovations, 8(4), 12568-12579.
22. Singh, A. (2025). Wi-Fi 8 as a deterministic wireless platform for real-time and mission-critical applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(4), 12438-12447.
23. Gowda, M. K. S. (2024). Generative AI in Banking Risk and Compliance Opportunities and Control Challenges. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13946.
24. Makkena, B. (2025, December). Improving IoT Network Security with a Hybrid Model for IDS in Cloud Infrastructure. In 2025 IEEE Pune Section International Conference (PuneCon) (pp. 1-6). IEEE.
25. Sharma, K., Konudula, J., Srinivas, S., & Mamadiyarov, Z. (2025, August). Leveraging AI and ML to Customize Salesforce CRM for Industry-Specific Solutions. In 2025 International Conference on Intelligent and Secure Engineering Solutions (CISES) (pp. 1492-1497). IEEE.
26. Katta, T. B. (2025, April). AI-Enhanced Orchestration in Hybrid Cloud Enterprise Integration: Transforming Enterprise Data Flows. In International Conference of Global Innovations and Solutions (pp. 118-129). Cham: Springer Nature Switzerland.
27. Kotla, M. R. T. (2025). Enterprise integration lessons from four digital frontlines: A comparative analysis of modern IT ecosystems. International Journal of Research Publications in Engineering, Technology and Management, 8(3), 32–42.
28. Parasa, M. (2025). Creating hyper-personalized learning journeys using AI in SAP SuccessFactors LMS for individual development and business alignment. International Research Journal of Engineering & Applied Sciences, 13(4), 241–255. https://doi.org/10.55083/irjeas.2025.v13i04022
29. 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).
30. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (Special Publication 800-145). National Institute of Standards and Technology.
31. NIST. (2023). AI Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology.
32. Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence. MIT Sloan Management Review, 59(1), 1–17.
33. Panda, S. S. (2025). Redefining cloud-native performance: A technical evaluation of Microsoft Azure’s Cobalt 100 ARM-based virtual machines. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(2), 11815–11830.
34. Sharma, P., Chen, Y., & Park, J. H. (2021). Secure and scalable multi-tenant cloud architectures using intelligent resource management. Future Generation Computer Systems, 118, 145–158.
35. Varghese, B., & Buyya, R. (2018). Next generation cloud computing: New trends and research directions. Future Generation Computer Systems, 79, 849–861.
36. Goel, N. (2023). Cloud security: Leveraging hybrid models for secure data storage. Res Militaris, 13(4), 10070–10078.
37. Wang, S., Liang, Y., & Zhang, X. (2020). Explainable artificial intelligence for intelligent enterprise governance systems. IEEE Access, 8, 185340–185350.
38. Indurthy, V. S. K. (2025). ETL-Driven Data Integration for Enhanced Pharmaceutical Manufacturer Rebate Processing. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(1), 11606-11615.
39. Weill, P., & Ross, J. W. (2004). IT governance: How top performers manage IT decision rights for superior results. Harvard Business School Press.
