AI Risk Intelligence Compliance and Data Center Governance
Keywords:
Adaptive AI in GRC, Risk Intelligence Systems, Enterprise Risk Management, AI-Driven Risk Analytics, GRC Transformation, Risk-Based Decision Systems, Compliance as Risk Amplifier, Operational Risk Management, Intelligent Risk Modeling, Hybrid AI Analytics, Predictive Risk Insights, Strategic Risk Alignment, Autonomous Risk Monitoring, Data-Driven Governance, Risk Continuum Modeling, AI-Enabled Compliance, Enterprise Risk Strategy, Dynamic Risk Management, Control Optimization, Next-Gen GRC Systems.Abstract
Automation may cause traditional modes of operations and control assigned to the Governance, Risk, and Compliance (GRC) units of organizations to become limited, virtually dormant, and misunderstood. Organizations worldwide continuously face growing – and often conflicting – pressures to react and implement ever-evolving regulatory requirements and changes. Simultaneously, these organizations face real managing risks and incidents that can affect their operations. Risk intelligence is needed that allows the strategy and operational areas to complete their mandates: make the business successful while achieving an optimal level of risk.
Adaptive Artificial Intelligence, combined with more traditional analytical modalities and statistical business-as-usual modeling, can form a new operational paradigm to support the enterprises’ risk-no-risk continuum. The demand is for practical strategies and methods that can help organizations achieve pragmatic risk management across the enterprise, with the GRC areas clearly becoming enablers of that strategy rather than the exclusive means of managing everything related to risk. Compliance requirements become the amplifiers of risk rather than the possible triggers for incidents and breaches. Progress, experience, growth, and technology become the new vectors of support rather than the demand for an ever-expanding reservoir of controls.
References
1. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834.
2. Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., Anderson, H., Roff, H., Allen, G. C., Steinhardt, J., Flynn, C., Héigeartaigh, S. Ó., Beard, S., Belfield, H., Farquhar, S., … Amodei, D. (2020). Toward trustworthy AI development: Mechanisms for supporting verifiable claims. arXiv.
3. Mattaparthi, R. (2023). Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9077-9088.
4. Dawson, D., Schleiger, E., Horton, J., McLaughlin, J., Robinson, C., Quezada, G., Scowcroft, J., & Hajkowicz, S. (2021). Artificial intelligence: Australia's ethics framework. AI & Society, 36(2), 509–520.
5. European Union Agency for Cybersecurity. (2020). Threat landscape for artificial intelligence. ENISA.
6. Gill, N., Mathur, A., & Conde, M. V. (2022). A brief overview of AI governance for responsible machine learning systems. arXiv.
7. Pereira, K., Vinagre, J., Alonso, A. N., Coelho, F., & Carvalho, M. (2022, September). Privacy-preserving machine learning in life insurance risk prediction. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 44-52). Cham: Springer Nature Switzerland.
8. 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.
9. Jobin, A., Ienca, M., & Vayena, E. (2020). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 2(9), 389–399.
10. Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2022). Trustworthy artificial intelligence: A review. ACM Computing Surveys, 55(2), 1–38.
11. Leslie, D. (2020). Understanding artificial intelligence ethics and safety. The Alan Turing Institute.
12. Kolla, S. K. (2023). Learning Health Systems Machine Intelligence for Clinical Prediction and Healthcare Optimization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7955-7966.
13. Mäntymäki, M., Minkkinen, M., Birkstedt, T., & Viljanen, M. (2022). Defining organizational AI governance. AI and Ethics, 2(4), 603–609.
14. NIST. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology.
15. Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
16. OECD. (2021). OECD framework for the classification of AI systems. OECD Publishing.
17. Ponick, E., & Wieczorek, G. (2022). Artificial intelligence in governance, risk and compliance: Results of a study on potentials for the application of artificial intelligence in governance, risk and compliance. arXiv.
18. Ryan, M., & Stahl, B. C. (2021). Artificial intelligence ethics guidelines for developers and users: Clarifying responsibilities. Journal of Information, Communication and Ethics in Society, 19(1), 61–77.
19. Kolla, T., & Kolla, S. K. (2023). FHIR-Based Real-Time Healthcare Analytics using Unsupervised Learning. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11751.
20. Sherchan, W., Chen, S. A., Harris, S., Alam, N., Tran, K.-N., & Butler, C. J. (2020). Cognitive compliance: Assessing regulatory risk in financial advice documents. Proceedings of the AAAI Conference on Artificial Intelligence, 34(9), 13636–13637.
21. Stahl, B. C., Antoniou, J., Ryan, M., Macnish, K., & Jiya, T. (2021). Organisational responses to the ethical issues of artificial intelligence. AI & Society, 36(1), 23–37.
22. 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.
23. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
24. World Economic Forum. (2020). Empowering AI leadership: AI C-suite toolkit. World Economic Forum.
25. World Economic Forum. (2022). The global AI action alliance: Responsible AI toolkit. World Economic Forum.
26. 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.
27. Zicari, R. V., Brodersen, J., Brusseau, J., de Andrés, J. R., & others. (2021). Z-Inspection®: A process to assess trustworthy AI. IEEE Transactions on Technology and Society.
28. Aitken, M., Leslie, D., Ostmann, F., Pratt, M., & Briggs, J. (2021). Common regulatory capacity for AI: Practical guidance for regulators. The Alan Turing Institute.
29. Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2021). Guidelines for human-AI interaction. Communications of the ACM, 64(4), 82–89.
30. Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.
31. Bird, C., Fox, S., Dibble, M., Fariello, F., & others. (2020). Fairness-aware machine learning: Practical challenges and lessons learned. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, 1–12.
32. Cobbe, J., Lee, M. S. A., & Singh, J. (2021). Reviewing risk assessment in AI impact assessments. AI and Ethics, 1(4), 1–13.
33. Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
34. Di Vaio, A., Hassan, R., Palladino, R., & Escobar, O. (2022). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 145, 638–651.
35. Djenna, A., Harous, S., & others. (2022). Artificial intelligence for cyber security: A review. Journal of Information Security and Applications, 66, 103153.
36. Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., Conboy, K., Doyle, R., Dubey, R., Dutot, V., Felix, R., Goyal, D. P., Gustafsson, A., Hinsch, C., Jebabli, I., … Wamba, S. F. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
37. 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.
38. European Commission. (2021). Proposal for a regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Publications Office of the European Union.
39. Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2022). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 28(3), 1–16.
40. Gillis, A. S. (2022). Data governance in enterprise AI: Best practices and implementation strategies. Computer Weekly, 45(6), 24–30.
41. ISO/IEC. (2021). ISO/IEC 27001:2022 Information security, cybersecurity and privacy protection—Information security management systems—Requirements. International Organization for Standardization.
42. Bandi, V. D. V. K. (2023). MLOps frameworks for reliable model deployment in cloud data platforms. Journal of Artificial Intelligence and Big Data, 3(1), 84-101.
43. Klievink, B., Romijn, B.-J., Cunningham, S., & de Bruijn, H. (2021). Big data in the public sector: Uncertainties and readiness. Information Systems Frontiers, 23(2), 267–283.
44. Madnick, S., Wang, R., Lee, Y., & Zhu, H. (2021). Overview and framework for data and information quality research. Journal of Data and Information Quality, 13(2), 1–24.
45. Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2021). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172.
46. Reddy, V. A. R. (2022). Data-Driven Healthcare Operations: Architecting Unified Member, Provider, and Claims Intelligence Platforms. International Journal of Science, Research and Technology, 5(5), 8511-8521.
47. Nguyen, G., Dlugolinsky, S., Bobák, M., Tran, V., García, Á. L., Heredia, I., Malík, P., & Hluchý, L. (2020). Machine learning and deep learning frameworks and libraries for large-scale data mining: A survey. Artificial Intelligence Review, 52(1), 77–124.
48. Radanliev, P., De Roure, D., Walton, R., Van Kleek, M., Santos, O., Ani, U., & Cannady, S. (2020). AI cyber risk management in industry 4.0. IEEE Access, 8, 188910–188921.
49. Shneiderman, B. (2022). Human-centered AI: Three fresh ideas. AIS Transactions on Human-Computer Interaction, 14(3), 311–326.
50. Sun, T. Q., & Medaglia, R. (2021). Mapping the challenges of artificial intelligence in the public sector. Government Information Quarterly, 38(4), 101493.
51. Mangalampalli, B. M. (2022). Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform, 11.
52. Varshney, K. R. (2022). Trustworthy machine learning. Manning Publications.
53. Wang, Y., Kung, L., & Byrd, T. A. (2020). Big data analytics: Understanding its capabilities and potential benefits for organizations. Information & Management, 57(6), 103207.
54. Ali, S., Dyo, V., & Shafique, M. (2023). Artificial intelligence for cybersecurity: A systematic literature review. Computers & Security, 126, 103066.
55. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2020). Concrete problems in AI safety. Communications of the ACM, 63(7), 82–89.
56. Benbya, H., Pachidi, S., & Jarvenpaa, S. L. (2021). Artificial intelligence in organizations: Implications for information systems research. Journal of the Association for Information Systems, 22(2), 281–303.
57. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
58. Breck, E., Cai, S., Nielsen, E., Salib, M., & Sculley, D. (2020). The ML test score: A rubric for machine learning production readiness and technical debt reduction. Proceedings of the IEEE International Conference on Big Data, 1123–1132.
59. Buruk, O., & Erol, I. (2023). Artificial intelligence governance frameworks: A systematic literature review. AI and Ethics, 3(4), 1181–1198.
60. Chen, H., Chiang, R. H. L., & Storey, V. C. (2020). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 44(2), 1165–1188.
61. Deloitte. (2022). State of AI in the enterprise (5th ed.). Deloitte Insights.
62. Gasser, U., & Almeida, V. A. F. (2022). A layered model for AI governance. IEEE Internet Computing, 26(2), 58–62.
63. Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
64. Google Cloud. (2023). AI governance: Best practices for responsible artificial intelligence. Google Cloud.
65. Hagendorff, T. (2021). Machine learning and artificial intelligence ethics: A review of value alignment and responsible AI. AI & Society, 36(3), 813–829.
66. IBM. (2023). IBM AI governance framework: Building trustworthy AI systems. IBM Corporation.
67. ISACA. (2023). Artificial intelligence governance: A practical guide for boards and executives. ISACA.
68. Microsoft. (2022). Responsible AI Standard (Version 2). Microsoft Corporation.
69. NIST. (2022). Towards a standard for identifying and managing bias in artificial intelligence (NIST Special Publication 1270). National Institute of Standards and Technology.
70. Oracle. (2023). Enterprise AI governance: Managing risk, compliance, and trust. Oracle Corporation.
71. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
72. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, 33–44.
73. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J. F., & Dennison, D. (2020). Hidden technical debt in machine learning systems. Communications of the ACM, 63(7), 56–65.
74. Varshney, K. R. (2021). Engineering trustworthy AI systems. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15481–15489.
75. World Economic Forum. (2023). The AI governance alliance: Responsible AI framework. World Economic Forum.
76. Zhang, Y., Xiong, F., Fan, X., Gu, H., & Liu, Y. (2023). Artificial intelligence governance: Challenges, opportunities, and future directions. AI and Ethics, 3(2), 487–503.
Additional Files
Published
Issue
Section
License
Copyright (c) 2023 Dimitris Plexousakis (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The American Online Journal of Science and Engineering (AOJSE) is an open-access journal, and all published articles are made freely available to readers worldwide under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license governs the use, sharing, adaptation, and distribution of all content published in AOJSE and is designed to promote the broadest possible dissemination and reuse of scholarly research.
Creative Commons Attribution 4.0 International License (CC BY 4.0)
Under the Creative Commons Attribution 4.0 International License, any user is free to share, copy, and redistribute the published material in any medium or format, and to adapt, remix, transform, and build upon the material for any purpose, including commercial use, provided that appropriate credit is given to the original authors and source. The license cannot be revoked as long as the terms are followed.
When using or redistributing content published in AOJSE, users must provide proper attribution by citing the original authors' names, the article title, the journal name (AOJSE), the volume and issue number, the year of publication, and the DOI or URL of the article. Users must also indicate if any changes were made to the original work. Attribution must be provided in a reasonable manner but must not suggest that the authors or AOJSE endorse the user or their use of the material.
Author Rights and Copyright Retention
AOJSE respects and upholds the intellectual property rights of all contributing authors. Authors who publish in AOJSE retain full copyright over their work. By submitting a manuscript to AOJSE, authors grant the journal a non-exclusive, worldwide, royalty-free license to publish, reproduce, distribute, publicly display, and archive the article in print and electronic formats. This license allows AOJSE to make the article freely accessible to readers globally while ensuring that the authors remain the rightful owners of their work.
Authors are permitted to deposit their published articles in institutional repositories, personal websites, academic networking platforms, and preprint servers, provided that the original publication in AOJSE is acknowledged and properly cited. Authors may also reuse their published content in subsequent works, presentations, teaching materials, and grant applications without requiring prior permission from the journal.
Third-Party Content
Authors are solely responsible for obtaining written permission to reproduce any third-party material, including figures, tables, images, data, or excerpts from other publications, included in their manuscript. Evidence of such permissions must be provided to the editorial office upon request. AOJSE does not assume any responsibility for copyright infringement arising from the unauthorized inclusion of third-party content in published articles.
Permitted Uses
Under the CC BY 4.0 license, the following uses of AOJSE published content are freely permitted without prior written permission from the journal or authors, provided that proper attribution is given. Users may read, download, print, and distribute articles for personal, educational, or research purposes. Researchers may reuse data, figures, and findings for meta-analyses, systematic reviews, and secondary research. Educators may incorporate published articles into course materials, syllabi, and academic presentations. Journalists, policymakers, and practitioners may reference and cite published findings for professional and public interest purposes.
Prohibited Uses
While the CC BY 4.0 license is broad and permissive, users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Users may not misrepresent the original authorship of published content or imply endorsement by the original authors or AOJSE without explicit consent. Plagiarism, data fabrication, and any form of academic misconduct in the reuse of published material are strictly prohibited and are a violation of publication ethics standards.
Institutional and Repository Deposit Policy
AOJSE fully supports self-archiving and green open access. Authors are encouraged to deposit the final published version of their article, also known as the version of record, in institutional repositories, subject repositories, and open-access databases immediately upon publication. There is no embargo period. Authors should always link back to the original article on the AOJSE website and include the full citation and DOI when depositing their work in any repository.
Digital Preservation and Archiving
AOJSE is committed to the long-term digital preservation of all published content to ensure its permanent availability and accessibility. The journal utilizes the Open Journal Systems (OJS) platform, which supports internationally recognized digital archiving standards. AOJSE encourages participation in archiving networks such as LOCKSS (Lots of Copies Keep Stuff Safe) and CLOCKSS (Controlled LOCKSS) to safeguard published articles against data loss and ensure continued access for future generations of researchers and readers.
Disclaimer
The views, opinions, findings, and conclusions expressed in articles published in AOJSE are solely those of the authors and do not necessarily reflect the official position, policy, or views of the journal, its editorial board, or its affiliated organizations. AOJSE makes every effort to ensure the accuracy and integrity of published content but does not accept legal responsibility for any errors, omissions, or claims arising from the use of information published in the journal.
Contact for Licensing Inquiries
For any questions regarding the licensing terms, copyright, or permitted use of content published in AOJSE, please contact the editorial office through the journal website at https://aojse.org. The editorial team will be happy to assist authors, readers, and institutions with any licensing-related queries.
