Defining and measuring risk appetite is a complex task due to its inherently subjective nature, often relying on personal judgment rather than objective criteria. Risk appetite is broadly defined as the level of risk an organization is willing to accept to achieve its goals, but traditional methods using numerical scales or vague categories can be insufficient or misleading. The challenges include the subjective interpretation of risk, the limitations of traditional tools like risk matrices, and the potential legal implications of formal risk statements. The proposed approach to better assess risk appetite involves using graph database tools and expert knowledge to analyze responses to multiple-choice and free-response questions, with the potential use of large language models (LLMs) to interpret the sentiment behind these responses. This method aims to provide a more nuanced and context-specific understanding of risk appetite, aligning it with organizational behavior and strategic goals, despite the inherent difficulties and subjective biases involved in such assessments.