Adaptive Risk Governance Framework for Enterprise Compliance Analytics

Authors

  • Luca Bianchi Author

Keywords:

Intelligent Product Governance, Enterprise Risk Control, Compliance Analytics, Adaptive Risk Management, Product Lifecycle Governance, Governance Risk and Compliance (GRC), Enterprise Compliance, Intelligent Product Systems, Risk Analytics, Data Center Compliance, Enterprise Governance, Regulatory Compliance.

Abstract

This work, titled "Intelligent Product Governance Framework for Adaptive Enterprise Risk Control and Data Center Compliance Analytics," presents a governance framework for intelligent product systems within the context of enterprise-wide adaptive risk control and compliance analytics. Effective governance must account for three core elements: the principles underlying adaptive risk control, the roles and responsibilities of stakeholders across development and operations, and the data architecture that supports compliance analytics for deployed products and data centers.

Innovation in modern enterprises is sustained both by enhancing existing offerings and by introducing new systems and services. As these offerings grow more complex, they evolve into intelligent product systems whose deployment and operation must continuously meet shifting customer and societal expectations — introducing new categories of risk in the process. Adaptive risk control is therefore essential to guide intelligent product systems through this evolution while keeping risk within acceptable limits. At the same time, compliance has emerged as a key competitive differentiator, requiring enterprises to adopt dynamic, adaptive approaches to compliance analytics, governance, and risk control at scale.

To address these challenges, this work proposes an integrated framework that unifies governance, adaptive risk control, and compliance analytics for intelligent product systems across the enterprise.

References

1. Wirtz, B. W., Weyerer, J. C., & Sturm, B. J. (2020). The dark sides of artificial intelligence: An integrated AI governance framework for public administration. International Journal of Public Administration, 43(9), 818–829.

2. Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44(6), 101976.

3. Mashetty, S., Malempati, M., Paleti, S., Adusupalli, B., & Singireddy, J. (2025). A Multidisciplinary Framework for AI and Data-Driven Transformation in Taxation, Insurance, Mortgage Financing, and Financial Advisory: Integrating Cloud Computing, Deep Learning, and Agentic AI for Community-Centric Economic Development. Insurance, Mortgage Financing, and Financial Advisory: Integrating Cloud Computing, Deep Learning, and Agentic AI for Community-Centric Economic Development.

4. Dignam, A. (2020). Artificial intelligence, tech corporate governance and the public interest regulatory response. Cambridge Journal of Regions, Economy and Society, 13(1), 37–54.

5. Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30, 99–120.

6. Wieringa, M. A. (2020). What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. In M. Hildebrandt & C. Castillo (Eds.), Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 1–18). Association for Computing Machinery.

7. Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy artificial intelligence. Government Information Quarterly, 37(3), 101493.

8. Kummari, D. N., Singireddy, J., Sheelam, G. K., Nandan, B. P., Pandiri, L., Lakkarasu, P., & Dwaraka. (2025, August). Generative AI Models for Process Optimization in Semiconductor Wafer Design and Yield Prediction. In International Conference on Artificial Intelligence: Theory and Applications (pp. 220-233). Cham: Springer Nature Switzerland.

9. Ryan, M. (2020). In AI we trust: Ethics, artificial intelligence, and reliability. Science and Engineering Ethics, 26, 2749–2767.

10. Larsson, S. (2020). On the governance of artificial intelligence through ethics guidelines. Asian Journal of Law and Society, 7(3), 437–451.

11. Thiebes, S., Lins, S., & Sunyaev, A. (2021). Trustworthy artificial intelligence. Electronic Markets, 31, 447–464.

12. Morley, J., Elhalal, A., Garcia, F., Kinsey, L., Mökander, J., & Floridi, L. (2021). Ethics as a service: A pragmatic operationalisation of AI ethics. Minds and Machines, 31, 239–256.

13. Mökander, J., & Floridi, L. (2021). Ethics-based auditing to develop trustworthy AI. Minds and Machines, 31, 323–327.

14. Mökander, J., Morley, J., Taddeo, M., & Floridi, L. (2021). Ethics-based auditing of automated decision-making systems: Nature, scope, and limitations. Science and Engineering Ethics, 27, 44.

15. Metcalf, J., Moss, E., Watkins, E. A., Singh, R., & Elish, M. C. (2021). Algorithmic impact assessments and accountability: The co-construction of impacts. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 735–746.

16. Radu, R. (2021). Steering the governance of artificial intelligence: National strategies in perspective. Policy and Society, 40(2), 178–193.

17. Recharla, M. (2024). Antioxidants, Biological Markers, Catalase, Glutathione Peroxidase, Chronic Periodontitis, Saliva, Smokeless tobacco, Smoker. Frontiers in Health Informatics, 13(8), 4999.

18. Stix, C. (2022). Foundations for the future: Institution building for the purpose of artificial intelligence governance. AI and Ethics, 2, 463–476.

19. Wirtz, B. W., Weyerer, J. C., & Kehl, I. (2022). Governance of artificial intelligence: A risk and guideline-based integrative framework. Government Information Quarterly, 39(4), 101685.

20. Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023). Toward AI governance: Identifying best practices and potential barriers and outcomes. Information Systems Frontiers, 25, 123–141.

21. Chhillar, D., & Aguilera, R. V. (2022). An eye for artificial intelligence: Insights into the governance of artificial intelligence and vision for future research. Business & Society, 61(5), 1197–1234.

22. Gianni, R., Lehtinen, S., & Nieminen, M. (2022). Governance of responsible AI: From ethical guidelines to cooperative policies. Frontiers in Computer Science, 4, 873437.

23. Hickman, E., & Petrin, M. (2021). Trustworthy AI and corporate governance: The EU's ethics guidelines for trustworthy artificial intelligence from a company law perspective. European Business Organization Law Review, 22, 593–619.

24. Mashetty, S. (2025). LEVERAGING DEEP LEARNING, NEURAL NETWORKS, AND DATA ENGINEERING FOR INTELLIGENT MORTGAGE LOAN VALIDATION. INTERNATIONAL JOURNAL OF SOCIAL SCIENCE & INTERDISCIPLINARY RESEARCH ISSN: 2277-3630 Impact factor: 8.036, 14(04), 51-65.

25. Mukhopadhyay, S. (2022). InfoGram and admissible machine learning. Machine Learning, 111, 205–242.

26. Sharma, S. (2023). Trustworthy artificial intelligence: Design of AI governance framework. Strategic Analysis, 47(5), 443–464.

27. Fraser, H., & Bello y Villarino, J.-M. (2023). Acceptable risks in Europe's proposed AI Act: Reasonableness and other principles for deciding how much risk management is enough. European Journal of Risk Regulation, 14(4), 711–730.

28. Mökander, J., & Axente, M. (2023). Ethics-based auditing of automated decision-making systems: Intervention points and policy implications. AI and Society, 38, 153–171.

29. Almeida, P. G. R. de, & Santos Júnior, C. D. dos. (2024). Artificial intelligence governance: Understanding how public organizations implement it. Government Information Quarterly, 41(3), 102003.

30. Giudici, P., Centurelli, M., & Turchetta, S. (2024). Artificial intelligence risk measurement. Expert Systems with Applications, 235, 121220.

31. Adusupalli, B., Malempati, M., Paleti, S., Mashetty, S., & Singireddy, J. (2025). Integrated financial ecosystems: AI-driven innovations in taxation, insurance, mortgage analytics, and community investment through cloud, big data, and advanced data engineering. Journal of Information Systems Engineering and Management, 10, 1103-1117.

32. Halgamuge, M. N. (2024). From COBIT to ISO 42001: Evaluating cybersecurity frameworks for opportunities, risks, and regulatory compliance in commercializing large language models. Computers & Security, 144, 103964.

33. Batool, A., Zowghi, D., & Bano, M. (2025). AI governance: A systematic literature review. AI and Ethics, 5, 3265–3279.

34. responsible artificial intelligence governance: A review and research framework. (2025). Journal of Strategic Information Systems, 34(2), 101885.

35. Mökander, J., & Floridi, L. (2021). Ethics-based auditing to develop trustworthy AI. Minds and Machines, 31, 323–327.

Additional Files

Published

2026-08-12

How to Cite

Adaptive Risk Governance Framework for Enterprise Compliance Analytics. (2026). European Advanced Journal for Science & Engineering (EAJSE), 4(03). https://esa-research.org/index.php/eajse/article/view/197

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