Secure Enterprise Deployment of Generative AI through LLMOps Governance and Hybrid Cloud Infrastructure

The rapid adoption of generative artificial intelligence (AI) across enterprise environments has created new opportunities forautomation, knowledge management, intelligent decision-making, and customer service. However, deploying large languagemodels (LLMs) in enterprise settings introduces security, privacy, governance, and operational challenges associated with sensitivedata exposure, unauthorized model access, prompt injection, model supply-chain vulnerabilities, and inconsistent regulatorycompliance. Hybrid cloud infrastructure offers a flexible deployment architecture that combines private infrastructure withpublic cloud services, enabling organizations to balance scalability, data sovereignty, and computational efficiency. This researchinvestigates a secure enterprise deployment framework for generative AI by integrating LLMOps governance with hybrid cloudinfrastructure. The proposed framework incorporates model lifecycle management, identity and access controls, data protection,continuous monitoring, model evaluation, and policy-based deployment approval. Security mechanisms include retrieval accessrestrictions, prompt and output validation, encrypted communication, audit logging, and risk-based model selection. Theresearch methodology employs a controlled experimental environment to compare conventional LLM deployment practiceswith the proposed governance-oriented architecture. Evaluation metrics include security incident detection, unauthorized dataexposure, response quality, inference latency, compliance coverage, and operational overhead. The study establishes a systematicfoundation for assessing how integrated LLMOps governance and hybrid cloud controls can improve enterprise AI security whilemaintaining performance, scalability, and operational reliability across diverse organizational environments.