探讨大模型在关键系统治理中的应用前景与挑战
On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?
- 通过调研从业者，分析大模型在关键系统治理中的实践经验
- 发现安全使用大模型需跨领域协作与透明数据管理
- 适合政策制定者、研究人员及安全系统开发者参考
背景：自计算机出现以来，关键基础设施安全始终是重大关切，如今在网络安全战背景下尤为突出。保护对国家安全至关重要的使命关键系统（MCSs）需要快速且稳健的治理，但近期事件凸显应对挑战的日益困难。目标：基于先前研究显示生成式AI（如大语言模型，LLMs）在风险分析中的潜力，本研究旨在探索从业者对将生成式AI融入MCS IT治理的看法。目标是为研究人员、实践者和政策制定者提供可操作的见解与建议。方法：设计问卷，收集在MCS环境中开发和实施安全解决方案的从业者关于实践经验、担忧与期望的一手资料。结论与未来工作：研究结果表明，安全使用大模型进行MCS治理需跨学科协作。研究人员应设计面向监管的模型并强调可问责性；实践者强调数据保护与透明度；政策制定者则需建立统一的全球性人工智能框架，以确保基于大模型的MCS治理在伦理与安全上达标。
原文摘要 · Abstract (English)
Context. The security of critical infrastructure has been a pressing concern since the advent of computers and has become even more critical in today's era of cyber warfare. Protecting mission-critical systems (MCSs), essential for national security, requires swift and robust governance, yet recent events reveal the increasing difficulty of meeting these challenges. Aim. Building on prior research showcasing the potential of Generative AI (GAI), such as Large Language Models, in enhancing risk analysis, we aim to explore practitioners' views on integrating GAI into the governance of IT MCSs. Our goal is to provide actionable insights and recommendations for stakeholders, including researchers, practitioners, and policymakers. Method. We designed a survey to collect practical experiences, concerns, and expectations of practitioners who develop and implement security solutions in the context of MCSs. Conclusions and Future Works. Our findings highlight that the safe use of LLMs in MCS governance requires interdisciplinary collaboration. Researchers should focus on designing regulation-oriented models and focus on accountability; practitioners emphasize data protection and transparency, while policymakers must establish a unified AI framework with global benchmarks to ensure ethical and secure LLMs-based MCS governance.
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