Next-Generation Enterprise AI: From Foundation Models to Federated and Secure Cloud Systems
Year: 2025
Type: preprint
Abstract: The rapid evolution of artificial intelligence in enterprise settings has ushered in a new era characterized by foundation models, federated learning, and secure cloud-based architectures. Foundation models, pre-trained on massive datasets, enable enterprises to leverage generalized AI capabilities across diverse applications, from natural language processing and computer vision to predictive analytics. However, the deployment of such large-scale AI systems introduces significant challenges, including computational demands, data privacy concerns, model interpretability, and operational scalability. Federated learning has emerged as a promising solution to mitigate privacy risks by enabling decentralized model training without centralizing sensitive enterprise data, thereby supporting collaboration across departments or organizations while maintaining regulatory compliance. Concurrently, secure cloud systems provide the infrastructure necessary to scale AI deployments efficiently while implementing robust security mechanisms such as encryption, identity-based access control, and Zero Trust principles. This paper explores strategies for integrating foundation models, federated learning, and secure cloud architectures within enterprise AI frameworks, highlighting best practices for MLOps, governance, auditing, and risk management. It also addresses the organizational, technical, and regulatory challenges associated with nextgeneration AI adoption and proposes frameworks to operationalize AI responsibly and securely. By synthesizing insights from distributed AI architectures, cloud-native infrastructures, and ethical AI principles, this study provides a roadmap for enterprises seeking to deploy AI systems that are not only scalable and efficient but also secure, privacy-preserving, and compliant with evolving regulatory standards.
Author: Elevane Dave
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413919615