As Artificial Intelligence (AI) systems become increasingly integrated into critical domains, conventional risk management methodologies often prove inadequate for addressing their unique and complex challenges, particularly the emergence of novel, unforeseen risks. To address this gap, this paper introduces the LLM-Based AI Risk Management Framework, a structured four-step process that systematically leverages Large Language Models (LLMs) to enhance risk identification and analysis. The framework’s efficacy is demonstrated through a detailed case study of an AI-powered matching system and an empirical validation of its core prompt engineering techniques. The results show that this approach enables the generation of comprehensive risk scenarios, including critical compliance and ethical issues initially overlooked by human experts, thereby serving as an objective counter-perspective to organizational biases. The study reveals that the framework’s success hinges on a sophisticated human-in-the-loop model where human experts provide strategic direction, not just passive validation. A key finding is that the quality of LLM outputs is dramatically improved by framing requests as concrete ‘incident scenarios’ instead of abstract ‘risks’. This research contributes an empirically-grounded methodology for integrating LLMs into AI governance, demonstrating that the strategic partnership between human expertise and LLM capabilities can foster a more robust, responsible, and safe approach to managing AI systems.