Governed AI Agents for Secure Enterprise Automation
Keywords:
Governed Enterprise Intelligence, Generative Agents, Policy-Driven Automation, Information Technology Governance, Security-by-Design, Responsible AI, Risk Assessment, Secure-by-Design Solutions, Technology Risk Management.Abstract
Governed Enterprise Intelligence – the monitored use of information by organizations for operational advantage – is crucial to navigating current turbulence affecting enterprises. Large language model (LLM)-driven agent-based automation is an enterprise-intelligent approach facilitating optimal information use. However, due to LLM vulnerabilities, spontaneous agent automation poses security and governance challenges that risk undermining their operational advantages. Addressing these challenges greatly increases agent operational performance while deploying secure services.
An LLM-driven agent is an autonomous software application capable of engaging within human-language dialogue and supporting humans across inquiry and action tasks. Agents are outfitted with mission statements and executed plans drawn from coherent information, which enable reasoning and action across simple and complex sets of sub-tasks. Complex tasks are achieved through multi-Agent collaboration, and Agents source and verify external service information from human users and available systems.
References
1. Alomari, M. K., Khan, H. U., & Khan, S. (2021). Systematic analysis of artificial intelligence-based platforms for identifying governance and access control. Security and Communication Networks, 2021, Article 8686469.
2. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2021). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
3. Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., & Dafoe, A. (2021). Toward trustworthy AI development: Mechanisms for supporting verifiable claims. arXiv preprint arXiv:2004.07213.
4. Inala, R. (2023). Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 579-606.
5. Chen, C., Li, K., Duan, Z., & Chen, X. (2021). Towards responsible artificial intelligence: A review of AI governance frameworks and principles. IEEE Access, 9, 148527–148545.
6. Jobin, A., Ienca, M., & Vayena, E. (2021). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 3(5), 389–399.
7. Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2022). Trustworthy artificial intelligence: A review. ACM Computing Surveys, 55(2), Article 39.
8. Aitha, A. R. (2021). Dev Ops Driven Digital Transformation: Accelerating Innovation In The Insurance Industry. Journal of International Crisis and Risk Communication Research.
9. Mökander, J., & Floridi, L. (2021). Ethics-based auditing to develop trustworthy AI. Minds and Machines, 31, 323–327.
10. Mökander, J., Morley, J., Taddeo, M., & Floridi, L. (2022). Ethics-based auditing of automated decision-making systems: Nature, scope, and limitations. Science and Engineering Ethics, 28, Article 44.
11. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
12. Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2021). From what to how: An initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics, 26, 2141–2168.
13. Siau, K., & Wang, W. (2021). Artificial intelligence, machine learning, automation, and robotics. Journal of Database Management, 32(1), 1–15.
14. Tabassi, E. (2021). Artificial intelligence risk management framework: Concept paper. National Institute of Standards and Technology.
15. Aravind, R., Surabhi, S. N. D., & Shah, C. V. (2023). Remote Vehicle Access: Leveraging Cloud Infrastructure for Secure and Efficient OTA Updates with Advanced AI. Eur. Econ. Lett, 13, 1309-1319.
16. Thiebes, S., Lins, S., & Sunyaev, A. (2021). Trustworthy artificial intelligence. Electronic Markets, 31, 447–464.
17. Vakkuri, V., Kemell, K.-K., Jantunen, M., & Abrahamsson, P. (2021). “This is just a prototype”: How ethics are considered in software engineering practice. IEEE Software, 38(5), 63–70.
18. Inala, R. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493-5505.
19. Yigitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2022). Contributions and risks of artificial intelligence in the governance of smart cities. Smart Cities, 5(2), 900–923.
20. Zhang, D., Maslej, N., Brynjolfsson, E., Etchemendy, J., Ganguli, D., Lyons, T., Manyika, J., Niebles, J. C., Sellitto, M., Shoham, Y., Clark, J., & Perrault, R. (2022). The AI Index 2022 annual report. AI Index Steering Committee, Stanford Institute for Human-Centered Artificial Intelligence.
21. Aravind, R., Surabhi, S. N. D., & Shah, C. V. (2023). Remote Vehicle Access: Leveraging Cloud Infrastructure for Secure and Efficient OTA Updates with Advanced AI. EuropeanEconomic Letters (EEL), 13 (4), 1308–1319. Retrieved fromhttps.
22. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
23. Weidinger, L., Mellor, J., Rauker, T., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, A., Balle, B., Kasirzadeh, A., Kenton, Z., Hawkins, W., Stepleton, T., Biles, C., Barnett, B., Bansal, Y., Azzam, M., Gabriel, I., Isaac, W., & Gabriel, M. (2022). Taxonomy of risks posed by language models. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 214–229.
24. Ganguli, D., Askell, A., Schiefer, N., Liao, T. I., Liskovich, D., & Amodei, D. (2022). Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858.
25. Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Ganguli, D., Henighan, T., Joseph, N., Kadavath, S., Kernion, J., Conerly, T., Elhage, N., El-Showk, S., El-Showk, A., Fort, S., Hume, T., Johnston, S., & Kaplan, J. (2022). Constitutional AI: Harmlessness from AI feedback. arXiv preprint arXiv:2212.08073.
26. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., & Liang, P. (2022). On the opportunities and risks of foundation models. Communications of the ACM, 65(7), 36–47.
27. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
28. Weidinger, L., Uesato, J., Rauker, T., Griffin, C., Huang, P.-S., Cheng, M., Glaese, A., Balle, B., Kasirzadeh, A., Kenton, Z., Hawkins, W., Stepleton, T., Birhane, A., Gabriel, I., Isaac, W., & Amodei, D. (2021). Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359.
29. 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.
30. Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), Article 195.
31. Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., Grave, E., & Scialom, T. (2023). Augmented language models: A survey. Transactions on Machine Learning Research, 2023.
32. Privacy and security challenges for autonomous agents: A study of two social humanoid service robots. (2022). Proceedings of the 2022 IEEE International Conference on Cloud Computing Technology and Science, 272–279.
Additional Files
Published
Issue
Section
License
Copyright (c) 2024 European Advanced Journal for Science & Engineering (EAJSE)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Articles published in the European Advanced Journal for Science & Engineering (EAJSE) are made freely available online immediately upon publication under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is properly cited. Authors retain copyright of their work. By submitting to EAJSE, authors grant the journal the right of first publication. For details, visit: https://creativecommons.org/licenses/by/4.0/
