Authors : Anusha Kondam

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/vjdrthsh

DOI : https://doi.org/10.38124/ijisrt/25jun879

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Today, it is essential to maintain enterprise data security and adequate integration capabilities for business warp. The age of identifying and sharing much sensitive data within organizations capitalizing on limited protections has given way to the era where secure access controls can still result in huge stakes. Failure to secure data might result in compromised app security, and you can imagine financial or reputational damages after accessing any nonpublic asset. Worried about this, many organizations are moving towards enterprise-grade data security solutions that use the help of logical link machines. LLMs are secure networks or enclaves facilitating controlled data sharing amongst various systems and applications. They provide a safe place for storing, processing, and transferring data so that only authorized people can access the sensitive information. LLMs also help integrate data with multiple systems and applications within an organization. This is useful for better data management, workflow automation, and good decision-making powers. Enterprises need a complete solution to secure their data, and at the same time, they would prefer that zero human hands should touch it for integration. Doing so presents a good opportunity for businesses to earn the trust of their customers and be compliant with regulatory mandates. It also helps them build an in-depth defense fabric they can rely on in an ever- changing threat landscape.

Keywords : Secure Networks, Data Sharing, Workflow Automation, Data Management, Transferring Data.

References :

1. Jeong, C. (2023). A study on the implementation of generative ai services using an enterprise data-based llm application architecture. arXiv preprint arXiv:2309.01105.
2. Prakash, K., Rao, S., Hamza, R., Lukich, J., Chaudhari, V., & Nandi, A. (2024, June). Integrating LLMs into Database Systems Education. In Proceedings of the 3rd International Workshop on Data Systems Education: Bridging education practice with education research (pp. 33-39).
3. Chatterjee, P., Bose, R., Banerjee, S., & Roy, S. (2023). Enhancing data security of cloud based lms. Wireless Personal Communications, 130(2), 1123-1139.
4. VM, K., Warrier, H., & Gupta, Y. (2024). Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations. arXiv preprint arXiv:2404.10779.
5. Xie, L., Zhang, J., Li, Y., Wan, S., Zhang, X., Chen, M., ... & Wu, Z. (2024, July). Research and Application of Private Knowledge-based LLM in Standard Operating Procedure Scenarios of Enterprises. In Proceedings of the 2024 6th International Conference on Pattern Recognition and Intelligent Systems (pp. 82-86).
6. Truong, H. L., Vukovic, M., & Pavuluri, R. (2024, July). On Coordinating LLMs and Platform Knowledge for Software Modernization and New Developments. In 2024 IEEE International Conference on Software Services Engineering (SSE) (pp. 188-193). IEEE.
7. Schillaci, Z. (2024). LLM Adoption Trends and Associated Risks. In Large Language Models in Cybersecurity: Threats, Exposure and Mitigation (pp. 121-128). Cham: Springer Nature Switzerland.
8. Krishnamoorthy, G., Kurkute, M. V., & Sreeram, J. (2024). Integrating LLMs into AI-Driven Supply Chains: Best Practices for Training, Development, and Deployment in the Retail and Manufacturing Industries. Journal of Artificial Intelligence Research and Applications, 4(1), 592-627.
9. Brachman, M., El-Ashry, A., Dugan, C., & Geyer, W. (2024, May). How Knowledge Workers Use and Want to Use LLMs in an Enterprise Context. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-8).
10. Dash, B. (2024). Zero-Trust Architecture (ZTA): Designing an AI-Powered Cloud Security Framework for LLMs' Black Box Problems. Available at SSRN 4726625.
11. Xu, J., Wang, Q., Cao, Y., Zeng, B., & Liu, S. (2024). A general-purpose device for interaction with llms. arXiv preprint arXiv:2408.10230.
12. Choquet, G., Aizier, A., & Bernollin, G. (2024). Exploiting Privacy Vulnerabilities in Open Source LLMs Using Maliciously Crafted Prompts.
13. Kirova, V. D., Ku, C. S., Laracy, J. R., & Marlowe, T. J. (2024, March). Software engineering education must adapt and evolve for an llm environment. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (pp. 666-672).
14. Ullah, A., Qi, G., Hussain, S., Ullah, I., & Ali, Z. (2024). The role of llms in sustainable smart cities: Applications, challenges, and future directions. arXiv preprint arXiv:2402.14596.
15. Barn, B. S., Barat, S., & Sandkuhl, K. (2023, November). Adaptation of enterprise modeling methods for large language models. In IFIP Working Conference on The Practice of Enterprise Modeling (pp. 3-18). Cham: Springer Nature Switzerland.
16. Kurkute, M. V., Namperumal, G., & Selvaraj, A. (2023). Scalable Development and Deployment of LLMs in Manufacturing: Leveraging AI to Enhance Predictive Maintenance, Quality Control, and Process Automation. Australian Journal of Machine Learning Research & Applications, 3(2), 381-430.
17. Agarwal, N., Thakur, D., Krishna, K., Goel, P., & Singh, S. P. (2021). LLMS for Data Analysis and Client Interaction in MedTech. International Journal of Progressive Research in Engineering Management and Science (IJPREMS), 1(2), 33-52.
18. McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., ... & 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.
19. Mandvikar, S. (2023). Augmenting intelligent document processing (idp) workflows with contemporary large language models (llms). International Journal of Computer Trends and Technology, 71(10), 80-91.
20. Han, S., Buyukates, B., Hu, Z., Jin, H., Jin, W., Sun, L., ... & He, C. (2024, August). Fedsecurity: A benchmark for attacks and defenses in federated learning and federated llms. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5070-5081).
21. V. L. B. Sunkara, “An intelligent routing framework for high-traffic networks using deep learning,” Int. J. Innov. Res. Comput. Commun. Eng. (IJIRCCE), vol. 13, no. 3, pp. 1942–1949, Mar. 2025. [Online]. Available: https://doi.org/10.15680/IJIRCCE.2025.1303002
22. P. K. Sharma, “Leveraging generative artificial intelligence for real-time fraud detection in banking systems,” Int. J. Innov. Res. Sci. Eng. Technol. (IJIRSET), vol. 12, no. 1, pp. 693–700, Jan. 2023. [Online]. Available: https://doi.org/10.15680/IJIRSET.2023.1201113
23. R. K. Dabbir, “Advanced predictive analytics in supply chain management: The role of deep generative models,” Int. J. Innov. Res. Sci. Eng. Technol. (IJIRSET), vol. 13, no. 6, pp. 12421–12428, Jun. 2024. [Online]. Available: https://doi.org/10.15680/IJIRSET.2024.1306292
24. A. Musunuri, "Leveraging AI and Deep Learning for ECommerce Customer Segmentation," Int. J. Innov. Res. Sci. Eng. Technol., vol. 12, no. 6, pp. 9081–9096, Jun. 2023, doi: 10.15680/IJIRSET.2023.1206165
25. S. D. Kalagarla, "Enhancing user experience in ePermitting systems through AI-driven personalization," Int. J. Sci. Technol., vol. 16, no. 1, pp. 1–15, Jan.–Mar. 2025. [Online]. Available: https://www.ijsat.org/papers/2025/1/2923.pdf.