AI-Driven Data Mesh Governance Models for Smart Government Digital Transformation
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I4P132Keywords:
Autonomous AI Copilots, Enterprise IT Service Management (ITSM), Cognitive Automation Frameworks, AI Assistants vs. AI Copilots, Decision Autonomy Levels, ITSM Orchestration Architectures, Event-Driven Service Pipelines, Cross-Silo Workflow Automation, Intelligent Service Operations, AI-Driven Incident and Change Management, Enterprise Integration Patterns, ITSM Cockpit Architecture, Interoperable Service Platforms, Security-by-Design in ITSM, Measurable Service Performance Outcomes, Deployment Strategies for AI in IT Operations, End-to-End Workflow Automation, Cognitive Decision Support Systems, Scalable ITSM Modernization, Digital Service Governance ModelsAbstract
AI-driven Data Mesh governance is necessary for Smart Government digital transformation in 2025 as AI enables a shift from Data Lake and Data Warehouse centralization towards a decentralized Data Mesh paradigm. Data Mesh federates ownership, responsibility, and data as product thinking. Meshes operate perimeter-based access control over data, enabling data-driven innovation via third-party services. Correct designs can avoid classic problems of local silo implementation with ambiguous quality or interpretation. Local Data Product Owners curate data quality against factors such as Quality Assurance by Design or compliance with Interoperability Frameworks by Design and document factor provenance for auditability. Mesh governance design can be entirely centralized or more federated. Maturity for Smart Government depends on product richness and on open standards supporting interoperability with all local Data Products compliant with Data Minimization by Design – consequently requiring only an integration point for information system or legislative purposes. Smart Government digital transformation requires four fundamental pillars: a coherent AI strategy; development of a federated AI data ecosystem with Internal Data Product Providers completing a mesh of public services and complying with local Internal Data Products; a Digital Identity solution recognized by all Smart Government organizations; and the establishment of appropriate Data Literacy programs, Data Quality by Design, and Data Minimization by Design views by the Data Privacy Authority. Business maturity is a consequence of the previous development, determining the enrichment, curation, availability, and readiness of Data Products for external consumption in the Data-Driven Economy or Data-Driven Society, for which Smart Government acts as Internal Data Product Provider.
Downloads
References
[1] Dehghani, Z. (2022). Data mesh: Delivering data-driven value at scale. O’Reilly Media.
[2] Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/wjcmr/article/view/1376
[3] Dawes, S. S. (2010). Stewardship and usefulness: Policy principles for information.
[4] Nagubandi, A. R. (2025). Pioneering Self-Adaptive Ai Orchestration Engines For Real-Time End-To-End Multi-Counterparty Derivatives, Collateral, And Accounting Automation: Intelligence-Driven Workflow Coordination At Enterprise Scale. Lex Localis, 23(S6), 8598-8610.
[5] Batini, C., & Scannapieco, M. (2016). Data and information quality: Dimensions, principles and techniques. Springer.
[6] Babaiah, C., Dobriyal, N., Shamila, M., Aitha, A. R., Patel, S. P., & Upodhyay, D. (2025, December). Intelligent Fault Detection and Recovery in Wireless Sensor Networks Using AI. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1-6). IEEE.
[7] Benjamens, S., Dhunnoo, P., & Meskó, B. (2020). The state of artificial intelligence-based FDA-approved medical devices. NPJ Digital Medicine, 3, 118.
[8] Amistapuram, K. (2025). Agentic AI for Next-Generation Insurance Platforms: Autonomous Decision-Making in Claims and Policy Servicing. Journal of Marketing & Social Research, 2, 88-103.
[9] Bertsekas, D. P. (2012). Dynamic programming and optimal control (Vol. 1). Athena Scientific.
[10] Vajpayee, A., Khan, S., Gottimukkala, V. R. R., Sharma, D., & Seshasai, S. J. (2025). Digital Financial Literacy 4.0: Consumer Readiness for AI-Driven Fintech and Blockchain Ecosystems. International Insurance Law Review, 33(S5), 963-973.
[11] Brundage, M., Avin, S., Clark, J., et al. (2018). The malicious use of artificial intelligence. arXiv.
[12] Nigam, N., Sireesha, B., Ediga, P., Segireddy, A. R., & Bokde, S. (2025, December). Comparative Evaluation of Cloud Security Algorithms Using Multiple Classifiers with an Optimized Intrusion Detection System. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1-6). IEEE.
[13] Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19, 171–209.
[14] Pareyani, S., Goswami, S., Geetha, Y., Dimri, S. K., Niharika, D. S., & Amistapuram, K. (2025, December). Smart Resource Allocation in Wireless Sensor Networks Through AI Techniques. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1-6). IEEE.
[15] Vijaya Rama Raju Gottimukkala. (2025). Agentic AI for Next-Generation Cross-Border Payments: Contextual Learning in Transaction Routing. Journal of Informatics Education and Research, 5(4). Retrieved from https://jier.org/index.php/journal/article/view/3794
[16] Aitha, A. R., & Jyothi Babu, D. A. (2025). Agentic AI-Powered Claims Intelligence: A Deep Learning Framework for Automating Workers Compensation Claim Processing Using Generative AI. Available at SSRN 5505223.
[17] Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407.
[18] Nagubandi, A. R. (2025). Cryptocurrency Market Spillovers: Risk Contagion Across Global Financial Systems.
[19] European Parliament and Council of the European Union. (2016). General Data Protection Regulation (GDPR). Official Journal of the European Union.
[20] Yandamuri, U. S. AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology.
[21] Gentry, C. (2009). A fully homomorphic encryption scheme. Stanford University.
[22] Inala, R. (2025). A Unified Framework for Agentic AI and Data Products: Enhancing Cloud, Big Data, and Machine Learning in Supply Chain, Insurance, Retail, and Manufacturing. Eksplorium-Buletin Pusat Teknologi Bahan Galian Nuklir, 46(1), 1614-1628.
[23] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
[24] Dutta, P., Mondal, A., Vadisetty, R., Polamarasetti, A., Guntupalli, R., & Rongali, S. K. (2025). A novel deep learning rule-based spike neural network (SNN) classification approach for diagnosis of intracranial tumors. International Journal of Information Technology, 17(9), 5705-5712.
[25] He, J., Baxter, S., Xu, J., et al. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25, 30–36.
[26] Davuluri, P. S. L. N. (2024). AI-Driven Data Governance Frameworks for Automated Regulatory Reporting and Audit Readiness. Metallurgical and Materials Engineering, 30(4), 996–1010. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1936
[27] Holzinger, A. (2016). Interactive machine learning for health informatics. Springer.
[28] FinOps Strategies for AI-Enabled Real-Time Compliance Platforms in Cloud Native Environments. (2025). MSW Management Journal, 35(2), 2080-2088.
[29] IBM. (2023). Data fabric architecture overview. IBM Redbooks.
[30] Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
[31] European Parliament and Council. (2016). Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union.
[32] World Bank. (2021). World development report 2021: Data for better lives. World Bank Publications.
[33] Kumar, K. M., Parasar, A., Walia, A., Inala, R., & Thulasimani, T. (2025, August). Enhancing Risk Management Strategies in Financial Institutions Using CNN and Support Vector Regression. In 2025 5th Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-6). IEEE.
[34] Koller, D., & Friedman, N. (2009). Probabilistic graphical models. MIT Press.
[35] Rao, A. N., Garapati, R. S., Suganya, R. T., Kaliappan, A., & Kamaleshwar, T. (2025, August). Smart Solar Harvesting and Power Management in IoT Nodes Through Deep Learning Models. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.
[36] Liu, F., et al. (2025). Foundational architecture for AI agents in healthcare. Cell Reports Medicine, 6(10), 102374.
[37] Paleti, S., Baliyan, M., Aitha, A. R., Reddy, B. A., Bhadauria, G. S., & Sing, S. A. (2025, August). Graph—LSTM Hybrid Model for Improving Fraud Detection Accuracy in E-Commerce Financial Services. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.
[38] Moreau, L., & Groth, P. (2013). Provenance: An introduction to PROV. Morgan & Claypool.
[39] Nagabhyru, K. C., Rani, M., Reddy, D. S., & Krishnaraj, V. (2025, August). Machine Learning-Driven Fault Detection in Electric Vehicles via Hybrid Reinforcement Learning Model. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.
[40] Obermeyer, Z., & Emanuel, E. (2016). Predicting the future—Big data and clinical medicine. Nejm, 375, 1216–1219.
[41] Amistapuram, K. (2025). Generative Ai For Claims Exceptions And Investigations: Enhancing Resolution Efficiency In Complex Insurance Processes. Available At Ssrn 5785482.
[42] Pearl, J. (2009). Causality (2nd ed.). Cambridge University Press.
[43] 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.
[44] Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. NEJM, 380, 1347–1358.
[45] Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1–19. Retrieved from https://www.scipublications.com/journal/index.php/crph/article/view/1372
[46] Varri, D. B. S. (2024). Adaptive and Autonomous Security Frameworks Using Generative AI for Cloud Ecosystems. Available at SSRN 5774785.
[47] Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
[48] Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39.
[49] Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
[50] Sheller, M. J., Reina, G. A., Edwards, B., et al. (2020). Multi-institutional deep learning without sharing patient data. Brainlesion Workshop.
[51] guntupalli, r. (2025). explainable ai in clinical decision support: interpretable neural models for trustworthy healthcare automationexplainable ai in clinical decision support: interpretable neural models for trustworthy healthcare automation. tpm–Testing, Psychometrics, Methodology in Applied Psychology, 32(S9 (2025): Posted 15 December), 462-471.
[52] Shortliffe, E. H., & Sepúlveda, M. J. (2018). Clinical decision support in the era of AI. JAMA, 320(21), 2199–2200.
[53] Rongali, S. K. (2025, August). Deep Learning for Cybersecurity in Healthcare: A Mulesoft-Enabled Approach. In 2025 International Conference on Artificial Intelligence and Machine Vision (AIMV) (pp. 1-6). IEEE.
[54] Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning (2nd ed.). MIT Press.
[55] Siva Hemanth Kolla. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2489–2502. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4774
[56] Tsamados, A., Aggarwal, N., Cowls, J., et al. (2022). The ethics of algorithms. AI & Society, 37, 215–230.
[57] Sasi Kumar Kolla. (2023). Big Data–Driven Machine Learning Frameworks for Clinical Risk Prediction. International Journal of Medical Toxicology and Legal Medicine, 26(3 and 4), 44–59. Retrieved from https://ijmtlm.org/index.php/journal/article/view/1456
[58] Wooldridge, M. (2009). An introduction to multiagent systems (2nd ed.). Wiley.
[59] Bandi, V. D. V. K. (2023). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205. Retrieved from https://www.seejph.com/index.php/seejph/article/view/7057
[60] Zhang, A., Xing, L., Zou, J., & Wu, J. C. (2022). Shifting ML for healthcare to deployment. Nature Biomedical Engineering, 6, 1330–1345.
[61] Velangani Divya Vardhan Kumar Bandi. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011–1027. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1011-1027
[62] Benford, S., et al. (2009). Emergent multi-agent architectures. Autonomous Agents and Multi-Agent Systems, 18, 15–45.
[63] Enterprise-Scale Gen AI Orchestration Using Small LMs and LLM Agents for Intelligent ITSM and HRSD Automation in Enterprise Ecosystems. (2025). MSW Management Journal, 35(2), 1889-1897.
[64] Ferber, J. (1999). Multi-agent systems: An introduction. Addison-Wesley.
[65] Garapati, R. S., & Daram, D. S. B. (2025). AI-Enabled Predictive Maintenance Framework For Connected Vehicles Using Cloud-Based Web Interfaces. Available at SSRN 5524261.
[66] Kephart, J. O., & Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1), 41–50.
[67] Guntupalli, R. (2025). Federated Deep Learning for Predictive Healthcare: A Privacy-Preserving AI Framework on Cloud-Native Infrastructure. Vascular and Endovascular Review, 8(16s), 200-210.
[68] Huhns, M. N., & Singh, M. P. (1998). Readings in agents. Morgan Kaufmann.
[69] Nagabhyru, K. C., & Babu, A. J. Human In The Loop Generative AI: Redefining Collaborative Data Engineering For High Stakes Industries.
[70] Erl, T. (2016). Microservices design patterns. Prentice Hall.
[71] Gottimukkala, V. R. R. (2025). Generative AI for Exceptions and Investigations: Streamlining Resolution Across Global Payment Systems. Journal of International Commercial Law and Technology, 6(1), 969-972.
[72] Fowler, M. (2018). Refactoring (2nd ed.). Addison-Wesley.
[73] Segireddy, A. R. (2025). Generative Ai For Secure Release Engineering In Global Payment Network. Lex Localis: Journal of Local Self-Government, 23.
[74] Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design patterns. Addison-Wesley.
[75] Varri, D. B. S. V. (2025). Human-AI collaboration in healthcare security.
[76] Rieke, N., Hancox, J., Li, W., et al. (2020). Federated learning for digital health. NPJ Digital Medicine, 3, 119.
[77] Zaharia, M., et al. (2010). Spark: Cluster computing with working sets. HotCloud.
[78] Rongali, S. K., & Varri, D. B. S. (2025). AI in health care threat detection. World Journal of Advanced Research and Reviews, 25(3), 1784-1789.
[79] Lakshman, A., & Malik, P. (2010). Cassandra. ACM SIGOPS Operating Systems Review, 44(2), 35–40.
[80] Nagabhyru, K. C. (2025). Beyond Automation: The 2025 Role of Agentic AI in Autonomous Data Engineering and Adaptive Enterprise Systems.
[81] Stonebraker, M., & Çetintemel, U. (2005). One size fits all? ICDE Proceedings, 2–11.
[82] Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology, 227.
[83] Moreira, M. W. L., et al. (2018). IoT-based smart healthcare systems. Sensors, 18(4), 1155.
[84] Guntupalli, R. (2025). Multi-Cloud vs. Hybrid Cloud Security: Key Challenges and Best Practices. Hybrid Cloud Security: Key Challenges and Best Practices (November 21, 2025).
[85] Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. NIST.
[86] Garapati, R. S. (2025). An Intelligent IoT Security System: Cloud-Native Architecture with Real-Time AI Threat Detection and Web Visualization. Journal homepage: https://jmsronline. com, 2(06).
[87] World Health Organization. (2021). Ethics and governance of artificial intelligence for health. WHO Press.
[88] 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. https://doi.org/10.61841/turcomat.v15i3.15497
[89] Moreau, L., et al. (2015). The W3C PROV family of specifications. Future Generation Computer Systems, 29(7), 161–165.
[90] Rongali, S. K. (2025, August). AI-Powered Threat Detection in Healthcare Data. In 2025 International Conference on Artificial Intelligence and Machine Vision (AIMV) (pp. 1-7). IEEE.
[91] Jennings, N. R., & Wooldridge, M. (1998). Applications of intelligent agents. Springer.
[92] Van Roy, P. (2009). Self-management in distributed systems. IEEE Computer, 42(12), 40–47.
[93] Vardhan Kumar Bandi, V. D. (2024). Automated Feature Engineering Systems in Large-Scale Healthcare Data Environments. Journal of Neonatal Surgery, 13(1), 2127–2141. Retrieved from https://www.jneonatalsurg.com/index.php/jns/article/view/10004
[94] Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, adoption barriers and myths of open data. Information Systems Management, 29(4), 258–268.
[95] Pamisetty, A., Paleti, S., Adusupalli, B., Singireddy, J., Inala, R., & Nagabhyru, K. C. (2025, September). Explainable AI Systems for Credit Scoring and Loan Risk Assessment in Digital Banking Platforms. In 2025 IEEE 13th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (pp. 1478-1483). IEEE.
