Architecting Compliance Ready Artificial Intelligence for Regulated Digital Systems
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Abstract
The new areas of automated digital systems such as finance and healthcare are being rolled out using AI systems. However, regulatory compliance is a system peculiarity that is induced by numerous AI systems as a form of outside control. In this paper, an architectural design that will be proposed will be adherent to the recent trends according to which auditability, decision traceability, model reproducibility and deterministic behavior will be explicitly stated in AI system design. The quantitative analysis has made a comparison of the compliance-native AI architectures and the traditional architectures based on the different compliance metrics. Its results show compliance native systems have a higher auditability, traceability, reproducibility and incidental reduced propensity to a compliance incidence. The findings suggest that AI deployment that is reliable, regulation capable, and scalable can be done with the help of compliance-conscious architecture
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[1] Lu, Q., Zhu, L., Xu, X., & Whittle, J. (2022). Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2203.00905
[2] Cadet, E., Babatunde, L. A., Ajayi, J. O., Erigh, E. D., Obuse, E., Essien, I. A., & Ayanbode, N. (2024). Developing scalable compliance architectures for Cross-Industry regulatory alignment. Shodhshauryam International Scientific Refereed Research, 141–176. https://doi.org/10.32628/shisrrj2472145
[3] Radanliev, P. (2025). Privacy, ethics, transparency, and accountability in AI systems for wearable devices. Frontiers in Digital Health, 7, 1431246. https://doi.org/10.3389/fdgth.2025.1431246
[4] Boosa, S. (2023). Leveraging EKS and AWS ML Stack for Compliance-Ready AI in healthcare. International Journal of AI BigData Computational and Management Studies, 4(2). https://doi.org/10.63282/3050-9416.ijaibdcms-v4i2p110
[5] Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885. https://doi.org/10.1016/j.jsis.2024.101885
[6] Omogiate, P. M. (2023). Designing automated audit mechanisms to evaluate compliance of generative AI platforms with federal authorship and ownership disclosure requirements. International Journal of Science and Research Archive, 10(2), 1536–1549. https://doi.org/10.30574/ijsra.2023.10.2.1099
[7] Ramos, S., & Ellul, J. (2024). Blockchain for Artificial Intelligence (AI): enhancing compliance with the EU AI Act through distributed ledger technology. A cybersecurity perspective. International Cybersecurity Law Review, 5(1), 1–20. https://doi.org/10.1365/s43439-023-00107-9
[8] Oluoha, O. M., Odeshina, A., Reis, O., Okpeke, F., Attipoe, V., & Orieno, O. H. (2022). A unified framework for Risk-Based access control and Identity Management in Compliance-Critical Environments. Journal of Frontiers in Multidisciplinary Research, 3(1), 23–34. https://doi.org/10.54660/.ijfmr.2022.3.1.23-34
[9] Pery, A., Rafiei, M., Simon, M., & P, V. D. a. W. M. (2021). Trustworthy artificial intelligence and process mining: Challenges and opportunities. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2110.02707
[10] Daswin, D. S., & Alahakoon, D. (2021). An artificial intelligence life cycle: from conception to production. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2108.13861