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

Abstract

The rapid adoption of generative artificial intelligence (AI) across enterprise environments has created new opportunities for
automation, knowledge management, intelligent decision-making, and customer service. However, deploying large language
models (LLMs) in enterprise settings introduces security, privacy, governance, and operational challenges associated with sensitive
data exposure, unauthorized model access, prompt injection, model supply-chain vulnerabilities, and inconsistent regulatory
compliance. Hybrid cloud infrastructure offers a flexible deployment architecture that combines private infrastructure with
public cloud services, enabling organizations to balance scalability, data sovereignty, and computational efficiency. This research
investigates a secure enterprise deployment framework for generative AI by integrating LLMOps governance with hybrid cloud
infrastructure. 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 access
restrictions, prompt and output validation, encrypted communication, audit logging, and risk-based model selection. The
research methodology employs a controlled experimental environment to compare conventional LLM deployment practices
with the proposed governance-oriented architecture. Evaluation metrics include security incident detection, unauthorized data
exposure, response quality, inference latency, compliance coverage, and operational overhead. The study establishes a systematic
foundation for assessing how integrated LLMOps governance and hybrid cloud controls can improve enterprise AI security while
maintaining performance, scalability, and operational reliability across diverse organizational environments.