Applying Agentic AI to Risk Governance in Global Data Center Operations
Keywords:
Agent-based AI; self-healing systems; compliance; governance; resilience; data centers; risk management; autonomic loops; global issues; ecosystems; AI systems; agentic AI; privacy issues; ethics; trust; auto-remediation; monitoring; social responsibility; transparency; societal needs; safety; artificial intelligences; data centers; cybersecurity; digital ecosystem; technological development; information technology; cyberspace; information and communications; governance, risk, and compliance; risk; information systems; use of agentic AI; storage data center.Abstract
How can compliance ecosystems be designed as self-healing systems, resilient to breaches and capable of automatically preventing recurrences? Recent advances in agentic AI suggest technological solutions, even within current regulations. A case study in global risk governance for data centers demonstrates the research design, compliance ecosystem architecture, and three-dimensional self-healing anatomy: support, government, and control. Self-healing compliance ecosystems allow dynamic consumption of data in indicated modes and are self-healing in the enabling way of autonomic loops, embracing monitoring, remediation, and feedback. The design-supporting analysis suggests action-oriented responses to incidents, disaster recovery, and business continuity, while substantial performance improvements and lessons learned contribute to compliance resilience. Agentic AI allows an adaptive compliance ecosystem acting on behalf of a stakeholder body, enabling a self-healing compliance ecosystem.
References
1. Ahmad, A., Maynard, S. B., & Park, S. (2020). Information security strategies: Towards an organizational multi-strategy perspective. Journal of Intelligent Manufacturing, 31(6), 1431–1446.
2. Alcaraz, C., & Lopez, J. (2020). Cyber resilience for cloud and distributed systems: A systematic review. Computers & Security, 92, 101747.
3. Amistapuram, K., Pandiri, L., Raju, V. R., Paleti, S., Singireddy, S., & Sheelam, G. K. (2025). AI-Based Cloud Infrastructure and MLOps Frameworks for Scalable Data Engineering Across Banking and Insurance. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 186–192). IEEE. 2025 IEEE International Conference on Communication Networks and Computing (CNC). https://doi.org/10.1109/cnc68716.2025.11484532
4. Association for Computing Machinery. (2021). ACM Code of Ethics and Professional Conduct.
5. Benbya, H., Pachidi, S., & Jarvenpaa, S. L. (2021). Artificial intelligence in organizations: Implications for information systems research. Journal of the Association for Information Systems, 22(2), 281–303.
6. Gadi, A. L., Garapati, R. S., Inala, R., Singireddy, J., & Kapila, D. (2025, October). Robust Mutual Authentication for Distributed IoT Systems: Balancing Security and Efficiency. In International Conference on Microelectronics, Electromagnetics and Telecommunication (pp. 538-548). Cham: Springer Nature Switzerland.
7. Bhattacharya, S., & Wamba, S. F. (2022). Artificial intelligence for enterprise risk management: A systematic review. Decision Support Systems, 155, 113717.
8. Brundage, M., et al. (2020). Toward trustworthy AI development: Mechanisms for supporting verifiable claims. arXiv.
9. Cloud Security Alliance. (2021). Security guidance for critical areas of cloud computing.
10. Deloitte. (2023). State of AI in the enterprise.
11. Srikanth, T., Segireddy, A. R., & Elavarasi, S. A. (2025, October). STaSFormer-SGAD: Semantic Triplet-Aware Spatial Flow-Guided Spatio-Temporal Graph for Anomaly Detection in Surveillance Videos. In 2025 International Conference on Communication, Computer, and Information Technology (IC3IT) (pp. 1-7). IEEE.
12. Dwivedi, Y. K., et al. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research. International Journal of Information Management, 57, 101994.
13. European Union Agency for Cybersecurity. (2021). Threat landscape for cloud services.
14. Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Harvard Data Science Review, 4(1).
15. Gartner. (2023). Top strategic technology trends: AI governance.
16. Siva Hemanth Kolla, Narendra Mangala. (2025). DESIGNING AUTONOMOUS LLM AGENT FRAMEWORKS USING GEN AI PIPELINES TO ENHANCE CUSTOMER SERVICE MANAGEMENT AND KNOWLEDGE WORKFLOWS. Lex Localis - Journal of Local Self-Government, 23(S6), 9719–9733. https://doi.org/10.52152/rhxpbz87
17. Google Cloud. (2023). AI governance framework for enterprise systems.
18. IBM. (2023). Global AI adoption index 2023.
19. International Organization for Standardization. (2020). ISO 31000:2018 Risk management—Guidelines.
20. Kolla, S. K. (2025). Next-Generation Precision Healthcare: AI-Driven Clinical Intelligence, Predictive Analytics, and Adaptive Decision Support Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(4), 12539-12552.
21. International Organization for Standardization. (2021). ISO/IEC 27001:2022 Information security management systems.
22. International Organization for Standardization. (2023). ISO/IEC 42001:2023 Artificial intelligence management systems—Requirements with guidance for use.
23. Aitha, A. R. (2025). Introduction to Predictive Autonomy. Available at SSRN 5590571.
24. Mattaparthi, R. (2025). GenAI-Augmented Diagnostic Reasoning for Diesel Engine Fault Triage: A Large Language Model Framework for Technician Decision Support at Scale. Journal of Material Sciences & Manufacturing Research, 6(12), 1. https://doi.org/10.47363/jmsmr/2025(6)226
25. Jarrahi, M. H. (2023). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 66(2), 163–174.
26. Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63(1), 37–50.
27. Khatri, V., & Brown, C. V. (2020). Designing data governance. Communications of the ACM, 63(1), 58–67.
28. Kshetri, N. (2021). Artificial intelligence in cybersecurity. IT Professional, 23(4), 10–15.
29. Li, J., Liu, H., & Zhao, Y. (2022). AI-driven cybersecurity risk assessment in cloud computing environments. Computers & Security, 114, 102603.
30. Mangalampalli, B. M., Kolla, S. K., Bandi, V. D. V. K., Yandamuri, U. S., & Rani, P. S. (2025). Designing Intelligent Healthcare Ecosystems through Adaptive Data Integration and Autonomous Learning Systems. Vascular and Endovascular Review, 8(20s), 330-347.
31. Luo, X., Tong, S., Fang, Z., & Qu, Z. (2021). Frontiers: Machines vs. humans: The impact of AI on decision making. Marketing Science, 40(4), 611–619.
32. McKinsey & Company. (2023). The state of AI in 2023.
33. Microsoft. (2023). Responsible AI standard (Version 2).
34. Mangalampalli, B. M. Intelligent Data Profiling for Healthcare Data Lakes Using AI-Enhanced Analytics.
35. NIST. (2020). Security and privacy controls for information systems and organizations (SP 800-53 Rev. 5).
36. NIST. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0).
37. OECD. (2021). OECD framework for the classification of AI systems.
38. Oracle. (2023). AI governance for enterprises.
39. Mattaparthi, R. (2025). GenAI-Augmented Diagnostic Reasoning for Diesel Engine Fault Triage: A Large Language Model Framework for Technician Decision Support at Scale. Journal of Material Sciences & Manufacturing Research, 6(12), 1. https://doi.org/10.47363/jmsmr/2025(6)226
40. Panetta, K. (2023). Agentic AI and autonomous enterprise systems. Gartner Research.
41. Park, S., & Kim, G. (2021). Cloud governance framework for enterprise digital transformation. Future Generation Computer Systems, 117, 302–314.
42. Paschen, U., Pitt, L., & Kietzmann, J. (2020). Artificial intelligence: Building blocks and applications. Business Horizons, 63(2), 147–155.
43. Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141.
44. Raghunath Loganathan. (2025). AGENTIC AI FRAMEWORKS FOR AUTONOMOUS RISK DETECTION AND COMPLIANCE REMEDIATION IN ENTERPRISE DATA CENTER OPERATIONS. Lex Localis - Journal of Local Self-Government, 23(S6), 9672–9697. https://doi.org/10.52152/3f90ak91
45. Radanliev, P., et al. (2020). Cyber risk at the edge: Current and future trends. Future Internet, 12(4), 64.
46. Russell, S. (2021). Human-compatible artificial intelligence and risk governance. Communications of the ACM, 64(9), 56–63.
47. Sarker, I. H. (2021). AI-based cybersecurity: A comprehensive review. Journal of Big Data, 8(1), 149.
48. Kolla, S. H. (2025). Autonomous Agentic Frameworks for Enterprise Service Operations and Intelligent Process Automation. International Journal of Science, Research and Technology, 8(4), 14643-14655.
49. Schneider Electric. (2022). Data center modernization and operational resilience.
50. Sharma, R., Mithas, S., & Kankanhalli, A. (2022). Transforming decision-making with AI. MIS Quarterly Executive, 21(2), 109–126.
51. Statista. (2023). Global data center market outlook.
52. Lebcir, I., Mageswari, S. U., Bhosale, Y. H., Nagubandi, A. R., & Mahabooba, M. M. Agile Strategic Management in the Age of Disruption: Leveraging AI and Data Analytics for Competitive Advantage.
53. Sunyaev, A. (2020). Internet computing: Principles of distributed systems and emerging technologies. Springer.
54. Taddeo, M., & Floridi, L. (2021). How AI can be a force for good. Science, 361(6404), 751–752.
55. Bhasgi, S. S., Garapati, R. S., & Sasikala, M. (2025, October). Medical Image Fusion of Magnetic Resonance Imaging and Computed Tomography Using Learned Wavelet Complex Adapter. In 2025 International Conference on Communication, Computer, and Information Technology (IC3IT) (pp. 1-6). IEEE.
56. Topol, E. (2020). Deep medicine: Artificial intelligence and healthcare. Basic Books.
57. United Nations Educational, Scientific and Cultural Organization. (2021). Recommendation on the ethics of artificial intelligence.
58. Kolla, T. (2025). Anomaly Detection Models for Outlier Provider Behavior in Cost and Treatment Patterns. Journal of Computational Analysis & Applications, 34(12), 1204.
59. van der Aalst, W. (2021). Process mining and AI for intelligent business operations. Springer.
60. Varshney, K. R. (2022). Trustworthy machine learning. Communications of the ACM, 65(4), 56–65.
61. Vinuesa, R., et al. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11, 233.
62. Wang, Y., Kung, L., & Byrd, T. A. (2021). Big data analytics and AI for enterprise governance. Information & Management, 58(3), 103414.
63. Davuluri, P. N. (2019). Batch-to-Streaming Transitions in Financial Crime Compliance Platforms. International Journal Of Engineering And Computer Science, 8(12).
64. Weill, P., & Woerner, S. (2021). Future ready: The four pathways to capturing digital value. Harvard Business Review Press.
65. World Economic Forum. (2020). Global risks report 2020.
66. World Economic Forum. (2022). Global cybersecurity outlook 2022.
67. World Economic Forum. (2023). Global cybersecurity outlook 2023.
68. Reddy, V. A. R., & Kolla, S. K. (1984). Infrastructure-As-Code Practices For Regulated Healthcare Cloud Environments. Metallurgical and Materials Engineering, 30 (4), 1028–1042.
69. World Economic Forum. (2025). Global risks report 2025.
70. Xu, M., David, J. M., & Kim, S. H. (2021). The fourth industrial revolution: Opportunities and challenges. International Journal of Financial Research, 12(3), 1–10.
71. Yang, Q., Steinfeld, A., Rosé, C., & Zimmerman, J. (2020). Re-examining whether, why, and how human-AI interaction is uniquely difficult. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW1), 1–33.
72. Nagabhyru, K. C., & Babu, A. J. Human In The Loop Generative AI: Redefining Collaborative Data Engineering For High Stakes Industries.
73. Zhang, Y., Xiong, H., & Leatham, K. (2022). Explainable artificial intelligence for trustworthy decision support. Information Systems Frontiers, 24(5), 1545–1561.
74. Bandi, V. D. V. K. AI-Based Anomaly Detection Frameworks in Distributed Enterprise Data Systems.
75. Zhou, J., et al. (2020). Artificial intelligence for cloud computing: A survey. IEEE Transactions on Cloud Computing, 8(3), 1031–1047.
76. Institute of Electrical and Electronics Engineers. (2021). IEEE standard model process for addressing ethical concerns during system design.
77. Das, N., Qubeb, S. M. P., Amistapuram, K., & Yadav, R. K. (2025). Artificial Inteligence and Data Science. BR Publications.
78. International Telecommunication Union. (2022). AI for good global summit report.
79. Cloud Security Alliance. (2025). Agentic AI governance and security guidance.
80. International Association of Privacy Professionals. (2025). AI governance in the agentic era.
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