从治理规范到可执行控制：一种分层翻译方法用于代理AI的运行时防护
From Governance Norms to Enforceable Controls: A Layered Translation Method for Runtime Guardrails in Agentic AI
AI总结：
本文提出一种分层翻译方法，将治理标准转化为四层控制机制，通过采购代理案例展示如何将治理目标转化为可执行的运行时防护措施。
摘要：
代理AI系统具备计划、使用工具、维护状态和生成多步骤轨迹的能力，这些特性导致治理问题与单轮生成式AI有本质不同：重要风险在执行过程中产生，而非仅在模型开发或部署时。ISO/IEC 42001、ISO/IEC 23894、ISO/IEC 42005、ISO/IEC 5338、ISO/IEC 38507以及NIST AI风险管理体系等标准对代理AI具有高度相关性，但本身无法直接产生可执行的运行时防护措施。本文提出一种分层翻译方法，将标准导出的治理目标连接到四个控制层：治理目标、设计时间约束、运行时调解和保证反馈。它区分治理目标、技术控制、运行时防护和保证证据；引入控制元组和运行时可执行性评估标准用于分层分配；并通过采购代理案例研究演示该方法。核心主张较为温和：标准应指导控制在架构、运行时策略、人工升级和审计中的放置，而运行时防护仅适用于可观测、确定性和时间敏感性足够以证明执行时干预的控制。
英文摘要：
Agentic AI systems plan, use tools, maintain state, and produce multi-step trajectories with external effects. Those properties create a governance problem that differs materially from single-turn generative AI: important risks emerge dur- ing execution, not only at model development or deployment time. Governance standards such as ISO/IEC 42001, ISO/IEC 23894, ISO/IEC 42005, ISO/IEC 5338, ISO/IEC 38507, and the NIST AI Risk Management Framework are therefore highly relevant to agentic AI, but they do not by themselves yield implementable runtime guardrails. This paper proposes a layered translation method that connects standards-derived governance objectives to four control layers: governance objectives, design- time constraints, runtime mediation, and assurance feedback. It distinguishes governance objectives, technical controls, runtime guardrails, and assurance evidence; introduces a control tuple and runtime-enforceability rubric for layer assignment; and demonstrates the method in a procurement-agent case study. The central claim is modest: standards should guide control placement across architecture, runtime policy, human escalation, and audit, while runtime guardrails are reserved for controls that are observable, determinate, and time-sensitive enough to justify execution-time intervention.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
