The increasing use of Large Language Models (LLMs) offers significant opportunities across the engineering lifecycle, including requirements engineering, software development, process optimization, and decision support. Despite this potential, organizations face substantial challenges in assessing the risks associated with LLM use, resulting in inconsistent integration, unknown failure modes, and limited scalability. This paper introduces the LLM Risk Assessment Framework (LRF), a structured approach for evaluating the application of LLMs within Systems Engineering (SE) environments. The framework classifies LLM-based applications along two fundamental dimensions: autonomy, ranging from supportive assistance to fully automated decision making, and impact, reflecting the potential severity of incorrect or misleading model outputs on engineering processes and system elements. By combining these dimensions, the LRF enables consistent determination of corresponding risk levels across the development lifecycle. The resulting classification supports organizations in identifying appropriate validation strategies, levels of human oversight, and required countermeasures to ensure safe and transparent deployment. The framework thereby helps align the rapid evolution of AI technologies with established engineering principles of reliability, traceability, and controlled process integration. Overall, the LRF provides a basis for risk-aware adoption of LLMs in complex engineering environments and represents a first step toward standardized AI assurance practices in systems engineering.

翻译：大型语言模型（LLMs）的日益广泛应用为工程生命周期（包括需求工程、软件开发、过程优化和决策支持）带来了重大机遇。尽管存在这种潜力，各组织在评估与LLM使用相关的风险方面仍面临重大挑战，导致集成不一致、失效模式未知以及可扩展性有限。本文介绍了LLM风险评估框架（LRF），这是一种用于评估LLM在系统工程（SE）环境中应用的结构化方法。该框架沿两个基本维度对基于LLM的应用进行分类：自主性（从辅助支持到全自动决策）和影响（反映模型错误或误导性输出对工程过程和系统要素的潜在严重性）。通过结合这些维度，LRF能够在整个开发生命周期中一致地确定相应的风险等级。由此产生的分类支持组织识别适当的验证策略、人工监督级别以及所需的应对措施，以确保安全透明的部署。因此，该框架有助于将人工智能技术的快速发展与既定的可靠性、可追溯性和受控过程集成等工程原则相结合。总体而言，LRF为在复杂工程环境中风险感知地采用LLM提供了基础，并代表了系统工程中迈向标准化AI保障实践的第一步。