Abstract

Generative Artificial Intelligence (GenAI) is increasingly used in IT operations to support tasks such as incident analysis, vul-nerability remediation, infrastructure management, and software delivery. Its probabilistic nature, however, introduces risks in-cluding hallucinated outputs, insecure recommendations, and unintended data exposure, which can be unacceptable in regu-lated and mission-critical environments. Existing GenAI safety mechanisms focus mainly on content moderation and developer-centric controls and provide limited assurance about system-level correctness, contextual awareness, or risk-based execution. This paper proposes a multi-layered guardrail and validation framework for the safe and reliable use of GenAI in enterprise IT operations. The framework integrates prompt governance, post-generation validation, context-aware risk assessment, and decision gating with selective human oversight. Using realistic case study scenarios for vulnerability remediation, incident re-sponse, and infrastructure changes, the framework is evaluated with metrics such as operational correctness, hallucination de-tection, and risk mitigation. The results indicate that structured guardrails substantially reduce unsafe outputs while preserving most automation benefits, offering a practical foundation for responsible GenAI adoption in production IT systems.

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