Compliance-Aware Conversational AI for Regulated Enterprise Decision Support
Keywords:
Compliance Aware Conversational AI, Artificial Intelligence (AI)Abstract
Conversational AI systems are now used in various settings to improve decision-making in different fields like business, healthcare, finance, human resources, customer services, etc. Though conversational AI systems hold many benefits for improving productivity, their integration into an organizational environment poses many challenges regarding how to maintain organizational Compliance. Ideal conversational AI systems are designed to maximize user experience or response accuracy; however, in many systems, regulatory requirements are often neglected during conventional design.
This paper describes Compliance-Aware Conversational AI, a new Conversational AI system particularly designed for decision support in an enterprise environment. This system is equipped with a new decision support feature whereby decision output is checked for Compliance automatically, unlike other decision support systems in modern AI. This system features a hierarchical architecture in which high-level decision support is isolated from low-level language understanding and response generation. A new feature in Compliance-Aware Conversational AI is reward planning in decision support in enterprise decision support systems.
It is evaluating the proposed approach with enterprise scenarios basing on the simulation: financial advisory support, human resources policy guidance, and data access decision assistance. Experimental results show that compliance-aware conversational agents strongly reduce policy violations while maintaining a high response relevance and user satisfaction. For the visualization of the accuracy of responses, adherence to the Compliance, and interaction latency, it shows a favorable trade-off compared to an unconstrained model for conversational interactions. These results also point towards the path of integrating compliance awareness into conversational AI as a necessary next step for its safe and scalable deployment in regulated enterprise environments.
References
[1] Yan, R. (2018, July). " Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI. In IJCAI (Vol. 18, pp. 5520-5526).
https://www.ijcai.org/proceedings/2018/0778.pdf
[2] Xin, D., Ma, L., Liu, J., Macke, S., Song, S., & Parameswaran, A. (2018, June). Accelerating human-in-the-loop machine learning: Challenges and opportunities. In Proceedings of the second workshop on data management for end-to-end machine learning (pp. 1-4).
https://dl.acm.org/doi/abs/10.1145/3209889.3209897
[3] Graesser, A. C., Cai, Z., Morgan, B., & Wang, L. (2017). Assessment with computer agents that engage in conversational dialogues and trialogues with learners. Computers in Human Behavior, 76, 607-616.
https://www.sciencedirect.com/science/article/pii/S074756321730198X
[4] Greengard, S. (2016). Cybersecurity gets smart. Communications of the ACM, 59(5), 29-31.
a. https://dl.acm.org/doi/abs/10.1145/2898969
[5] Ibitoye, J. S. (2018). Securing smart grid and critical infrastructure through AI-enhanced cloud networking. International Journal of Computer Applications Technology and Research, 7(12), 517-529.
[6] van den Broek, T., & van Veenstra, A. F. (2018). Governance of big data collaborations: How to balance regulatory Compliance and disruptive innovation. Technological Forecasting and Social Change, 129, 330-338.
https://www.sciencedirect.com/science/article/pii/S0040162517314695
[7] Kingston, J. (2017). Using artificial intelligence to support Compliance with the General Data Protection Regulation. Artificial Intelligence and Law, 25(4), 429-443.
https://link.springer.com/article/10.1007/s10506-017-9206-9
[8] Sannidhanam, A. H. (2026). LLM-Driven Workflow Optimization: Intelligent Routing in Asynchronous Distributed Systems. International Journal of AI and Machine Learning, 1(2), 23–33.
[9] Distributed Data Processing Frameworks for Large-Scale Healthcare Analytics. (2019). International Journal of Advance Industrial Engineering, 7(04), 1–10. doi:10.14741/ijaie/v.7.4.01
[10] Anjani Haritha Sannidhanam. (2023). Zero-Downtime AI Model Updates in Real-Time Inference Systems. International Journal of Engineering Science & Humanities, 13(2), 70–78.
[11] Sannidhanam, A. H. (2021). Real-Time Claims Processing Using Event-Driven Architectures. International Journal of Technology, Management and Humanities, 7(01), 36–50. doi:10.21590/07.01.02
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Author
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
