Sarcouncil Journal of Multidisciplinary

Abstract: The use of Large Language Models on private organizational data marks an important advancement in enterprise artificial intelligence and is focused on the central challenge of using large language processing capabilities while keeping data safe and staying compliant. This in-depth review examines the multi-dimensional technical landscape for the enterprise use of foundation models, particularly architecture adaptations, privacy-preserving solutions, fine-tuning methods, and orchestration strategies. Foundation model architectures have evolved to safely accommodate private data use cases, primarily because of improvements to the attention mechanism, normalization methods, and context management properties. Retrieval-Augmented Generation architectures allow organizations to produce model outputs that are grounded in private/organizational knowledge bases while avoiding encoding private information in the model's parameters. Specific fine-tuning methods significantly lower compute requirements for downstream applications compared to retraining the model entirely. Agent-based orchestration frameworks create pathways for non-linear text generation, along with augmentation with tools, multi-step processes, and coordination with work streams. Privacy-preserving methods also represent a highly important consideration and lead to the need for differentially private model training methods, federated learning architectures, and hardware-based trusted execution environments. Access control, data use tracing, and full governance frameworks provide oversight into whether the model use is appropriate or lawful. Deployment strategies must consider competing priorities such as performance, reliability, cost, and security, whether the deployment is cloud-based, on-premises, or hybrid.