DIALLOps: A DevOps-Integrated Framework for Secure andReliable Large Language Model Operations

Abstract

The rapid integration of large language models (LLMs) into enterprise and mission-critical systems has introduced a new class of operational and cybersecurity challenges that extend beyond traditional software and machine learning deployments. While recent advances in LLM capabilities enable powerful generative applications, their probabilistic, non deterministic, and interaction-driven behaviour raises significant concerns related to reliability, hallucinations, governance, and secure deployment. Existing DevOps and MLOps frameworks, which assume deterministic execution and static evaluation, are insufficient to manage these risks in production environments. This paper proposes DIALLOps, a DevOps-Integrated Adaptive Lifecycle framework for Large Language Model Operations, designed to align generative AI systems with security-aware DevOps automation. DIALLOps introduces a unified operational abstraction that treats models, prompts, retrieval pipelines, runtime policies, and agent logic as first-class artifacts, enabling consistent versioning, deployment, rollback, and governance. The framework redefines continuous integration and deployment semantics for probabilistic systems through distribution-aware validation, continuous in production evaluation, and human-in-the-loop oversight. The proposed architecture combines evaluation, observability, governance, and cost-aware control within a centralized operational control plane, enabling proactive mitigation of hallucinations, behavioral anomalies, and policy violations. A prototype implementation of DIALLOps was developed using Python, Ollama, and the Mistral large language model. Experimental evaluation using 50 test cases and probabilistic consistency testing achieved a policy compliance rate of 90%, consistency scores ranging from 0.7267 to 0.9610, and successful automated deployment gating through governance-aware rollback mechanisms. The findings suggest that DIALLOps offers a practical and security-oriented foundation for deploying LLM-based systems reliably at scale, positioning LLMOps as a critical operational layer for trustworthy and secure generative AI adoption.