GENERATIVE AI-ENABLED COMPLIANCE DOCUMENTATION AND AUDIT TRAIL AUTOMATION FOR GLOBAL DATA CENTER GOVERNANCE
Main Article Content
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.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
1. Pamisetty, A. (2021). A comparative study of cloud platforms for scalable infrastructure in food distribution supply chains.
2. Kalisetty, S., & Singireddy, J. (2023). Optimizing Tax Preparation and Filing Services: A Comparative Study of Traditional Methods and AI Augmented Tax Compliance Frameworks. Available at SSRN 5206185.
3. Botlagunta, P. N., & Sheelam, G. K. (2020). Data-Driven Design and Validation Techniques in Advanced Chip Engineering. Global Research Development (GRD) ISSN, 2455-5703.
4. Kolla, S. H. (2024). RETRIEVAL-AUGMENTED GENERATION WITH SMALL LLMS FOR KNOWLEDGE-DRIVEN DECISION AUTOMATION IN ENTERPRISE SERVICE PLATFORMS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 476-486.
5. Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
6. Mangalampalli, B. M. Intelligent Data Profiling for Healthcare Data Lakes Using AI-Enhanced Analytics.
7. Yandamuri, U. S. AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology.
8. Sheelam, G. K., & Nandan, B. P. (2021). Machine Learning Integration in Semiconductor Research and Manufacturing Pipelines. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI, 10.
9. Kummari, D. N., & Burugulla, J. K. R. (2023). Decision Support Systems for Government Auditing: The Role of AI in Ensuring Transparency and Compliance. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 493-532.
10. Pamisetty, A. (2022). Big Data can Generate Major Opportunities for Manufacturing Supply Chains. International Journal of Scientific Research and Modern Technology, 1(12), 238–251. https://doi.org/10.38124/ijsrmt.v1i12.1186
11. Chakilam, C., Suura, S. R., Koppolu, H. K. R., & Recharla, M. (2022). From Data to Cure: Leveraging Artificial Intelligence and Big Data Analytics in Accelerating Disease Research and Treatment Development. Journal of Survey in Fisheries Sciences. https://doi. org/10.53555/sfs. v9i3, 3619.
12. Kolla, S. H. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications, 31(4).
13. Sheelam, G. K., & Koppolu, H. K. R. (2024). From Transistors to Intelligence: Semiconductor Architectures Empowering Agentic AI in 5G and Beyond. Journal of Computational Analy- sis and Applications(JoCAAA), 33(08), 4518-4537.
14. Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
15. Nagabhyru, K. C. (2024). Data Engineering in the Age of Large Language Models: Transforming Data Access, Curation, and Enterprise Interpretation. Computer Fraud and Security.
16. Koppolu, H. K. R., Recharla, M., & Chakilam, C. Revolutionizing Patient Care with AI and Cloud Computing: A Framework for Scalable and Predictive Healthcare Solutions. Pr (y= 1| x)= s (w⊤ x+ b), 1.
17. Meda, R. (2024). Agentic AI in Multi-Tiered Paint Supply Chains: A Case Study on Efficiency and Responsiveness. Journal of Compu-tational Analysis and Applications (JoCAAA), 33(08), 3994-4015.
18. Meda, R. (2021). Digital Infrastructure for Predictive Inventory Management in Retail Using Machine Learning. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI, 10.
19. Sheelam, G. K. (2024). Deep Learning-Based Protocol Stack Optimization in High-Density 5G Environments. European Advanced Journal for Science & Engineering (EAJSE)-p-ISSN, 3050-9696.
20. Davuluri, P. N. (2019). Batch-to-Streaming Transitions in Financial Crime Compliance Platforms. International Journal Of Engineering And Computer Science, 8(12).
21. Amistapuram, K. (2024). Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning. Available at SSRN 6143426.
22. Meda, R. (2021). Machine Learning-Based Color Recommendation Engines for Enhanced Customer Personalization. Machine Learning, 4(S4).
23. Uday Surendra Yandamuri. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706. https://doi.org/10.5281/zenodo.18095256
24. Pamisetty, V. (2023). Leveraging AI, Big Data, and Cloud Computing for Enhanced Tax Compliance, Fraud Detection, and Fiscal Impact Analysis in Government Financial Management. Fraud Detection, and Fiscal Impact Analysis in Government Financial Management (December 15, 2023).
25. Inala, R., & Somu, B. (2024). Agentic AI in Retail Banking: Redefining Customer Service and Financial Decision-Making. Journal of Artificial Intelligence and Big Data Disciplines, 1(1).
26. Pamisetty, V. (2024). AI-Driven Decision Support for Taxation and Unclaimed Property Management: Enhancing Efficiency through Big Data and Cloud Integration. Available at SSRN 5250776.
27. Garapati, R. S. (2022). Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health, 2, 1346.
28. Inala, R. (2022). Cross-Domain MDM Integration Using AI-Driven Data Governance: A Case Study In Financial Technology Architecture. Migration Letters, 19(2), 280-304.
29. Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674.
30. Pamisetty, V. (2023). Leveraging artificial intelligence for strategic decision-making in tax administration and policy design. Available at SSRN 5276644.
31. Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
32. Bandi, V. D. V. K. (2023). MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms.
33. Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
34. Nandan, B. P. (2022). AI-Powered Fault Detection In Semiconductor Fabrication: A Data-Centric Perspective.
35. Singireddy, S. (2023). Integrating Deep Learning and Machine Learning Algorithms in Insurance Claims Processing: A Study on Enhancing Accuracy, Speed, and Fraud Detection for Policyholders. Educ. Adm. Theory Pract. https://doi. org/10.53555/kuey. v29i4, 9668.
36. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
37. Kolla, S. K. (2024). Federated Machine Learning On Big Healthcare Data For Privacy-Preserving Analytics. The Review of Diabetic Studies, 175-190.
38. Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
39. Kummari, D. N. (2021). A Framework for Risk-Based Auditing in Intelligent Manufacturing Infrastructures. International Journal on Recent and Innovation Trends in Computing and Communication, 9(12), 245-262.
40. Reddy Segireddy, A. (2024). Federated Cloud Approaches for Multi-Regional Payment Messaging Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(2), 442-450.
41. Bandi, V. D. V. K. (2024). AI-Driven Predictive Risk Modeling Architectures for Financial Systems. International Journal Of Finance, 37(3), 54-78.
42. Divya, V., & Bandi, V. K. (2023). Cloud-Native Model Lifecycle Management for Enterprise AI Systems. International Journal of Scientific Research and Modern Technology, 78.
43. Singireddy, J. (2024). Ai-enhanced tax preparation and filing: Automating complex regulatory compliance. European Data Science Journal (EDSJ) p-ISSN, 3050-9572.
44. Recharla, M. (2024). Advances in Therapeutic Strategies for Alzheimer’s Disease: Bridging Basic Research and Clinical Applications. American Online Journal of Science and Engineering (AOJSE)(ISSN: 3067-1140), 2(1).
45. Mangalampalli, B. M. (2024). AI-Enhanced Data Governance: Automating Compliance In Healthcare Analytics Platforms. The Review of Diabetic Studies, 191-204.
46. O'Mahony, N., Murphy, T., Panduru, K., Riordan, D., & Walsh, J. (2016, December). Machine learning algorithms for process analytical technology. In 2016 World Congress on Industrial Control Systems Security (WCICSS) (pp. 1-7). IEEE.
47. Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research , 101–122. https://doi.org/10.63278/jicrcr.vi.3738
48. Kolla, T. (2024). AI-Powered Data Catalog Systems For Healthcare Data Discovery And Governance. South Eastern European Journal of Public Health, 2296–2311. https://doi.org/10.70135/seejph.vi.7077
49. Malempati, M., Pandiri, L., Paleti, S., & Singireddy, J. (2023). Transforming financial and insurance ecosystems through intelligent automation, secure digital infrastructure, and advanced risk management strategies. Jeevani, Transforming Financial And Insurance Ecosystems Through Intelligent Automation, Secure Digital Infrastructure, And Advanced Risk Management Strategies (December 03, 2023).
50. Davuluri, P. N. (2020). Event-Driven Architectures for Real-Time Regulatory Monitoring in Global Banking.
51. Keerthi Amistapuram. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950–5958. https://doi.org/10.53555/kuey.v29i4.10965
52. Gottimukkala, V. R. R. (2022). Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology, 1(12), 177-186.
53. Pandiri, L., & Singireddy, S. (2023). AI and ML Applications in Dynamic Pricing for Auto and Property Insurance Markets. Journal for ReAttach Therapy and Developmental Diversities, 6, 2206-2223.
54. Aitha, A. R. (2021). Dev Ops Driven Digital Transformation: Accelerating Innovation In The Insurance Industry. Available at SSRN 5622190.
55. Kolla, S. K. (2023). Explainable AI and ML Models for Transparent Clinical Decision Support. Journal for ReAttach Therapy and Developmental Diversities, 6, 2444-2460.
56. Kolla, S. H. (2022). Knowledge Retrieval Systems for Enterprise Service Environments. International Journal of Intelligent Systems and Applications in Engineering, 10, 495-506.
57. Mukesh, A., & Aitha, A. R. (2021). Insurance Risk Assessment Using Predictive Modeling Techniques. International Journal of Emerging Research in Engineering and Technology, 2(4), 68-79.
58. Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
59. Bandi, V. D. V. K. Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics.
60. Pamisetty, A., Adusupalli, B., Mashetty, S., & Singreddy, S. (2024). Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management. Sneha, Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management (December 05, 2024).
61. Kummari, D. N. (2021). Smart Infrastructure Auditing: Integrating AI to Streamline Manufacturing Compliance Processes. Journal of Interna-tional Crisis and Risk Communication Research, 168-193.
62. Valiki, D., & Segireddy, A. R. (2023). Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction. American International Journal of Computer Science and Technology, 5(5), 12-24.
63. Meda, R. (2022). Integrating IoT and Big Data Analytics for Smart Paint Manufacturing Facilities. Kurdish Studies.
64. Nagabhyru, K. C. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898-5910.
65. Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Available at SSRN 5532601.
66. Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20. https://doi.org/10.31586/jaibd.2021.1361
67. Davuluri, P. N. Streaming Data Architectures For Sanctions Screening And Fraud Intelligence. JEC PUBLICATION.
68. Gottimukkala, V. R. R. (2023). Privacy-Preserving Machine Learning Models for Transaction Monitoring in Global Banking Networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 633-652.
69. Sheelam, G. K. (2024). Towards autonomic wireless systems: integrating agentic AI with advanced semiconductor technologies in telecommunications. Am. Online J. Sci. Eng., 3(4), 234-256.