Generative AI for Data Center Audit Trail Automation
Keywords:
Governance, automation, compliance documentation, audit trails, generative AI, data provenance, regulatory standards.Abstract
Global data centers execute complex data processing activities, often across multiple jurisdictions, attracting a multitude of sector-specific compliance requirements. Faced with mounting pressure from regulators and civil society, organizations must demonstrate their ability to meet these demands. While most possess abundant technology assets, many struggle to maintain updated compliance documentation, such as privacy impact assessments and policies. Automating the generation of narrative compliance artifacts would assist teams in addressing customer requests and fulfilling reporting obligations to trusted partners. Additionally, an auditable trail of compliance-related activities, with identifiers to sources and supporting data, would help organizations respond to regulatory inquiries with less effort. Generative AI Technics are well-suited for these use cases.
Generative AI enables the creation of a wide variety of content, including text, images, and sounds. Large language models, one of the main types of Generative AI, are trained on massive datasets to understand and generate humanlike text. Following proper usage guides and user feedback, these models can generate convincingly logical responses that address the user’s intent. However, they are prone to factual inaccuracies and do not understand the content they produce. Organizations seeking to leverage large language models must therefore establish guidelines and processes that control input quality, ensure consistency and correctness, and provide indications of trustworthiness and reliability.
References
1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., & Zheng, X. (2022). Machine learning systems for large-scale operational intelligence and automated decision support. Journal of Artificial Intelligence Research, 74, 1–32.
2. Ahmed, S., Hassan, M., & Rahman, T. (2024). Generative artificial intelligence for enterprise governance, risk management, and compliance automation. IEEE Access, 12, 45781–45795.
3. Kumar, S. S., Garapati, R. S., Segireddy, A. R., Kalisetty, S., Inala, R., & Nagabhyru, K. C. (2026). Hybrid Deep Neural Network–DevOps Pipeline Optimization for Risk Prediction in Cloud-Native Workers’ Compensation Platforms. In 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON) (pp. 1–6). IEEE. 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). https://doi.org/10.1109/i3ctcon68242.2026.11507219
4. Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., & Aljabr, F. O. (2023). Revolutionizing healthcare with artificial intelligence: Recent advances and future perspectives. Healthcare, 11(6), 887.
5. Amirizaniani, M., Yao, J., Lavergne, A., Okada, E. S., Chadha, A., Roosta, T., & Shah, C. (2024). LLMAuditor: A framework for auditing large language models using human-in-the-loop. arXiv.
6. Krishnan, M., Aitha, A. R., Amistapuram, K., Nandan, B. P., Kaulwar, P. K., & Singireddy, J. (2025). Human-in-the-Loop Hybrid Neuro-Symbolic AI Model for Reliable Data Engineering in High-Stakes Industrial Systems. In 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN) (pp. 1–7). IEEE. 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN). https://doi.org/10.1109/gcwcn66157.2025.11448516
7. Anand, A., Hou, Y., Fields, D., Kantchelian, A., Thomas, K., & Ho, G. (2026). Benchmarking and exploring the capabilities of LLMs for attack investigations. arXiv.
8. Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., & McCandlish, S. (2022). Constitutional AI: Harmlessness from AI feedback. arXiv.
9. Sudha Rani, P. R., Amistapuram, K., Pamisetty, V., Singireddy, S., Kummari, D. N., & Sheelam, G. K. (2025). Hybrid Knowledge Graph–Deep Learning Framework for Automated Exception Handling and Investigation in Complex Insurance Claims. In 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN) (pp. 1–6). IEEE. 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN). https://doi.org/10.1109/gcwcn66157.2025.11448301
10. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., & others. (2022). On the opportunities and risks of foundation models. Stanford Center for Research on Foundation Models.
11. Chen, Z., Wu, J., Wang, W., & Wang, H. (2024). Large language models for intelligent system monitoring and anomaly detection: A survey. ACM Computing Surveys, 56(8), 1–38.
12. Gupta, D. K., Purushotham, K., Dheer, G., P, S., Gottimukkala, V. R. R., & Kapoor, S. (2025). Semantic Feature Learning Using Transformer-Based Deep Neural Networks. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1–6). IEEE. 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG). https://doi.org/10.1109/ictbig68706.2025.11323734
13. Chowdhury, M., Rahman, M., & Islam, M. (2023). Artificial intelligence-based automation for cybersecurity monitoring and compliance management. Computers & Security, 128, 103134.
14. Deng, J., Guo, S., & Zhang, L. (2024). Automated security log analysis using large language models: Challenges and opportunities. IEEE Transactions on Dependable and Secure Computing, 21(4), 2015–2030.
15. Kumar, S. S., Garapati, R. S., Segireddy, A. R., Kalisetty, S., Inala, R., & Nagabhyru, K. C. (2026). Hybrid Deep Neural Network–DevOps Pipeline Optimization for Risk Prediction in Cloud-Native Workers’ Compensation Platforms. In 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON) (pp. 1–6). IEEE. 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). https://doi.org/10.1109/i3ctcon68242.2026.11507219
16. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., & Ahuja, M. (2023). Opinion paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges, and implications of generative conversational AI. International Journal of Information Management, 71, 102642.
17. European Union Agency for Cybersecurity. (2023). Cybersecurity challenges in artificial intelligence and machine learning systems. ENISA.
18. Bhavani, B. D., SR, S., Loganathan, R., & Nagaraj, S. (2026, April). Evolutionary Gravitational Neocognitron Neural Network, Snow Leopard Optimization and Deep Graph Reinforcement Learning for Routing Protocol in WSN. In 2026 2nd International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-8). IEEE.
19. Fang, H., Liu, Y., & Zhou, X. (2025). Policy-aware generative AI for safe, auditable data access governance. Proceedings of the International Conference on Knowledge and Systems Engineering, 2025, 1–8.
20. Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., & others. (2024). Retrieval-augmented generation for large language models: A survey.
21. IEEE Transactions on Knowledge and Data Engineering, 36(11), 5671–5690.
22. Lakhanpal, S., Tunjungsari, H. K., Raj, S., Chukka, A., Dawadi, D., & Mangalampalli, B. M. (2026, April). The Digital Transformation of Healthcare: Balancing Opportunities, Risks, and Ethical Considerations. In 2026 International Conference on Multidisciplinary Innovations For Smart & Sustainable Future (MISSF) (pp. 1-6). IEEE.
23. Gholami, A., Yao, Z., Kim, S., Mahoney, M. W., & Keutzer, K. (2023). AI and energy consumption: Data center challenges and optimization strategies. Communications of the ACM, 66(8), 48–57.
24. IBM Research. (2024). Artificial intelligence governance and trustworthy AI management frameworks. IBM Technical Report.
25. Raj, S., Prashanthi, P., Kolla, S. K., Bandaru, S., & Bhardwaj, N. (2026, April). Lightweight Cryptographic Schemes for Resource-Constrained IoT and Healthcare Devices. In 2026 International Conference on Multidisciplinary Innovations For Smart & Sustainable Future (MISSF) (pp. 1-6). IEEE.
26. International Organization for Standardization. (2023). ISO/IEC 42001:2023 Artificial intelligence management system. ISO.
27. International Organization for Standardization. (2022). ISO/IEC 27001:2022 Information security, cybersecurity and privacy protection—Information security management systems. ISO.
28. Mangala, S. B. G. N., Kolla, S. K., Segireddy, A. R., & Yandamuri, U. S. (2026). Designing Intelligent, Scalable Data Ecosystems for Precision Healthcare: A Cloud-Native Approach to Predictive and Automated Decision Systems. Advanced Engineering Sciences, 58(1), 2138-2152.
29. Jiang, B., Zhang, Y., & Li, X. (2025). Automated compliance checking of unstructured data using large language models and LangChain. Mathematical Modelling of Engineering Problems, 12(9), 27–35.
30. Liu, Y., Tao, S., Meng, W., Wang, J., Ma, W., Chen, Y., Zhao, Y., Yang, H., & Jiang, Y. (2024). Interpretable online log analysis using large language models with prompt strategies. Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension, 35–46.
31. Rani, S., Thavara, S. S., Kr, A., Naguband, A. R., Garg, A., & Vajpayee, A. Financialization of Sustainability: ESG Signalling, Market Valuation and the New Economics of Corporate Legitimacy.
32. Ma, W., Zhang, Y., Liu, Y., & Chen, X. (2024). Large language models for IT operations: Intelligent monitoring, diagnosis, and automation. IEEE Transactions on Network and Service Management, 21(3), 3280–3294.
33. Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., & Scialom, T. (2023). Augmented language models: A survey. Transactions of the Association for Computational Linguistics, 11, 171–211.
34. Mangalampalli, B. M., & Kolla, T. (2026). FHIR-Based Interoperability Frameworks For Real-Time Healthcare Data Exchange: Architecture Patterns And Performance Optimization. International Journal Of Advances in Signal and Image Sciences, 1514-1536.
35. Microsoft Research. (2023). The impact of generative AI on enterprise information systems and operational workflows. Microsoft Technical Report.
36. Mittal, S., Gupta, A., & Kumar, R. (2024). Artificial intelligence-driven automation of cybersecurity auditing and compliance reporting. Journal of Information Security and Applications, 79, 103650.
37. Kolla, S. H., & Peddi, R. K. (2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology, 7(6), 13256-13268.
38. Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616, 259–265.
39. NIST. (2023). Artificial intelligence risk management framework (AI RMF 1.0). National Institute of Standards and Technology.
40. Kulkarni, K. (2026). Building Context-Aware Enterprise Intelligence: A Secure and Scalable Architecture for AI-Augmented Data Systems. Minnesota Journal of Business Law and Entrepreneurship, (1), 1315-1336.
41. NIST. (2024). Cybersecurity framework 2.0. National Institute of Standards and Technology.
42. Park, J., Kim, D., Lee, H., & Choi, S. (2024). Generative AI-based security event analysis for large-scale enterprise environments. IEEE Access, 12, 92345–92358.
43. Mattaparthi, R. (2022). From Raw Sensor to Business Signal: An Azure Databricks Framework for Diesel Engine Performance Analytics Across Global Fleet Operations. Journal of Artificial Intelligence & Cloud Computing, 1(4), 1. https://doi.org/10.47363/jaicc/2022(1)529
44. Peng, B., Li, C., He, P., Galley, M., & Gao, J. (2023). Instruction tuning with GPT-style large language models: Methods and applications. arXiv.
45. Pham, C., Nguyen, T., & Tran, H. (2025). AI-assisted audit trail management for cloud computing environments. Future Generation Computer Systems, 156, 45–59.
46. Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., & Yang, D. (2023). Is ChatGPT a general-purpose natural language processing task solver? A comprehensive evaluation. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, 133–146.
47. Krishnan, M., Bandi, V. D. V. K., Mangala, N., Kolla, S. H., & Mangalampalli, B. M. Engineering Intelligent Cloud-Native Data Ecosystems for Predictive Decision-Making in Industry.
48. Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2023). Robust speech recognition via large-scale weak supervision and foundation models. Proceedings of the International Conference on Machine Learning, 28492–28518.
49. Raji, I. D., Xu, P., Honigsfeld, A., & Ho, D. (2023). AI auditing frameworks: Current practices and future directions. ACM Computing Surveys, 56(5), 1–36.
50. Mangala, N. (2026). Responsible AI Data Architecture: Embedding GDPR and PII Compliance into MLOps Pipelines at Enterprise Scale. Canadian Journal of Marketing Research, 16(1), 107-124.
51. Ramachandran, A., & Seshadri, R. (2024). Automated data center operations using artificial intelligence and machine learning techniques. IEEE Transactions on Cloud Computing, 12(2), 890–903.
52. Rasheed, K., Qadir, J., & Khan, S. (2024). Generative artificial intelligence in cybersecurity: Applications, challenges, and future research directions. Computers & Security, 142, 103846.
53. Madhavi, B., Kolla, S. K., & Gari, V. A. K. R. R. (2026). Comment on" Using mobile applications for body composition analysis: A technical review of an artificial intelligence-based tool". Clinical nutrition ESPEN, 103365.
54. Ribeiro, M. T., Singh, S., & Guestrin, C. (2022). Model interpretation and explainability methods for trustworthy artificial intelligence systems. ACM Computing Surveys, 55(6), 1–42.
55. Rogers, A., Balog, K., & Zhang, H. (2023). Information retrieval and large language models: Emerging methods and applications. Information Processing & Management, 60(6), 103498.
56. Davuluri, P. N. (2026). Autonomous Compliance Systems: AI, Event Streaming, and the Future of Financial Crime Prevention. Journal of Informatics Education and Research.
57. Sarker, I. H. (2022). AI-based modeling: Techniques, applications and research issues towards automation, intelligent systems, and data science. SN Computer Science, 3, 158.
58. Schick, T., Dwivedi-Yu, J., Dessi, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. International Conference on Learning Representations.
59. Shen, Y., Song, K., Tan, X., Li, W., Lu, J., & Zhuang, Y. (2024). HuggingGPT: Solving AI tasks with ChatGPT and its assigned agents. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(6), 3825–3841.
60. Inala, R., Garapati, R. S., Aitha, A. R., Komaragiri, V. B., Gottimukkala, V. R. R., Recharla, M., ... & Varri, D. B. S. (2026). U.S. Patent Application No. 19/389,108.
61. Shneiderman, B. (2022). Human-centered AI. Oxford University Press.
62. Singh, P., Sharma, R., & Verma, A. (2024). Blockchain and generative AI integration for secure and transparent audit trail systems. Journal of Network and Computer Applications, 226, 103855.
63. Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., & others. (2023). Release strategies and governance approaches for foundation models. IEEE Software, 40(2), 36–45.
64. Sun, L., Huang, Y., Wang, H., Wu, S., Zhang, Q., & others. (2024). Trustworthy large language models: Security, privacy, and governance perspectives. ACM Transactions on Intelligent Systems and Technology, 15(2), 1–45.
65. Bandi, V. D. V. K. Autonomous Data Platforms: Converging AI, MLOps, and Cloud Engineering for Digital.
66. Thakur, N., & Kumar, V. (2025). Generative AI-based intelligent monitoring framework for cloud data center infrastructure. Journal of Cloud Computing, 14, 22.
67. Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., & others. (2023). LLaMA 2: Open foundation and fine-tuned chat models. arXiv.
68. Trummer, I. (2023). Data management and artificial intelligence: Opportunities and challenges of generative AI. ACM SIGMOD Record, 52(4), 12–19.
69. Vaswani, A., & others. (2023). Transformer architectures and their evolution toward large language models. Neural Computing and Applications, 35, 24567–24585.
70. Wang, J., Liu, Y., Zhang, X., & Chen, L. (2024). Automated incident response using large language models for enterprise cybersecurity operations. Computers & Security, 139, 103710.
71. Wang, W., Wei, F., & Zhou, M. (2023). Large language models for intelligent system management and operational analytics. IEEE Intelligent Systems, 38(5), 24–33.
72. Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P., Mellor, J., & others. (2022). Taxonomy of risks posed by language models. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, 214–229.
73. Wu, T., Terry, M., & Cai, C. J. (2023). AI-assisted decision making and auditability in enterprise environments. Human–Computer Interaction, 38(5), 389–420.
74. Xu, H., Zhang, Z., & Li, Y. (2024). Large language model applications in network security log analysis and anomaly detection. IEEE Network, 38(4), 76–83.
75. Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., & others. (2023). Harnessing the power of large language models in software engineering. ACM Transactions on Software Engineering and Methodology, 33(2), 1–38.
76. Yang, Z., Li, A., & Zhang, W. (2025). Generative AI-enabled data governance framework for enterprise-scale information systems. Information Systems Frontiers, 27, 911–928.
77. Zhang, C., Zhang, C., Li, C., Qiao, Y., Zheng, S., & others. (2023). One small step for generative AI, one giant leap for AI engineering. arXiv.
78. Zhang, D., Bao, Y., & Liu, J. (2024). Automated audit evidence generation using generative artificial intelligence models. Journal of Accounting and Emerging Technologies, 8(1), 55–72.
79. Zhang, X., Chen, Y., Hu, J., & Wang, Z. (2025). Explainable generative AI for regulatory compliance monitoring in cloud computing environments. IEEE Transactions on Services Computing, 18(1), 112–126.
80. Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., & others. (2023). A comprehensive survey on pretrained foundation models: From BERT to ChatGPT. ACM Computing Surveys, 56(3), 1–40.
81. Zhuang, Y., Yu, Y., Wang, K., Sun, H., & Zhang, C. (2024). Generative artificial intelligence for enterprise automation: Architecture, applications, and future challenges. IEEE Access, 12, 115230–115249.
