From Lakehouse to Compliance Fabric: Building a Unified Cloud Data Ecosystem for Generative AI in Complex Corporate Tax Strategy

Authors

  • Deepak Reddy Suram Senior Software Engineer & Cloud Data Architect, USA Author

DOI:

https://doi.org/10.15662/IJEETR.2026.0801007

Keywords:

Lineage Completeness, Corporate Tax Analytics, Compliance Fabric, Lakehouse Architecture, Generative AI Transparency

Abstract

The paper provides an evaluation of how reliability, transparency, and reduce reproducibility of the corporate tax burdens can be increased by extending a lakehouse with the help of a compliance fabric, especially when the Generative AI models are utilized to interpret the rules. The paper focuses on comparing an improved system with a baseline architecture along the ingestion, lineage tracking, policy enforcement and reproducibility on the basis of quantitative metrics. The results have shown that there are major improvements in validated-record ratios, completeness in lineage, accuracy of policy options, and consistent Generative AI output. The working on the workloads was reduced since the metadata-based validation reduced the manual reconciliation. The multi-jurisdiction simulations also evolved into being stable and susceptible to tax regulations. The tax data operationalization with the help of the AI-driven compliance fabric is more defensible, traceable, and efficient

References

[1] Chandra, J., & Navneet, S. K. (2025). Policy-Driven AI in Dataspaces: Taxonomy, explainability, and Pathways for Compliant Innovation. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2507.20014

[2] Chen, Z., Wang, Y., & Zhao, X. (2025). Responsible Generative AI: governance challenges and solutions in enterprise data clouds. Journal of Computing and Electronic Information Management, 18(3), 59–65. https://doi.org/10.54097/02teq773

[3] Peram, P. (2025). A FABRIC-FIRST LAKEHOUSE ARCHITECTURE: a COMPREHENSIVE FRAMEWORK FOR SCALABLE ANALYTICS AND GENERATIVE AI. INTERNATIONAL JOURNAL OF ENGINEERING AND TECHNOLOGY RESEARCH, 10(2), 45–50. https://doi.org/10.34218/ijetr_10_02_004

[4] Perugu, P. K. (2025). AI-Driven solutions for data governance in Multi-Cloud ecosystems. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5119378

[5] Simhadri, S. Y. (2025). THE FUTURE OF AI-DRIVEN DATA ARCHITECTURE: NAVIGATING TRENDS, TALENT, AND TRANSFORMATION. In THE FUTURE OF AI-DRIVEN DATA ARCHITECTURE: NAVIGATING TRENDS, TALENT, AND TRANSFORMATION (pp. 86–97). https://doi.org/10.58532/nbennurapsfsw9

[6] Mandalawi, S. A., Mohammed, M. A., Maclean, H., Cakmak, M. C., & Talburt, J. R. (2025). Policy-Aware Generative AI for safe, auditable data access governance. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2510.23474

[7] Garouani, M., Ravat, F., & Valles-Parlangeau, N. (2024). Model Lake : a new alternative for Machine learning models management and governance. In Lecture notes in computer science (pp. 133–144). https://doi.org/10.1007/978-981-96-0573-6_10

[8] Gebler, R., Reinecke, I., Sedlmayr, M., & Goldammer, M. (2025). Enhancing clinical data infrastructure for AI research: Comparative Evaluation of Data Management Architectures. Journal of Medical Internet Research, 27, e74976. https://doi.org/10.2196/74976

[9] Wanigasooriya, S. (2025). Implement a Unified Data Integration & Analysis Platform : A Case Study. Implement a Unified Data Integration &Amp; Analysis Platform : A Case Study. https://doi.org/10.13140/rg.2.2.12757.95208

[10] Koukaras, P. (2025). Data integration and storage strategies in heterogeneous analytical systems: architectures, methods, and interoperability challenges. Information, 16(11), 932. https://doi.org/10.3390/info16110932

Downloads

Published

2026-02-17

How to Cite

From Lakehouse to Compliance Fabric: Building a Unified Cloud Data Ecosystem for Generative AI in Complex Corporate Tax Strategy. (2026). International Journal of Engineering & Extended Technologies Research (IJEETR), 8(1), 52-60. https://doi.org/10.15662/IJEETR.2026.0801007