发表机构
National Institute of Technology Rourkela（鲁尔基国立技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对大语言模型和人工智能驱动智能体发展带来的软件治理问题，提出基于OpenTelemetry构建多层次人工智能治理堆栈Traccia，通过添加遥测数据等解决对齐问题，自动创建合规证据包，为企业管理自主人工智能系统创建机器可读基础。
AI 中文摘要
大语言模型和人工智能驱动的自主智能体的快速发展从根本上改变了软件治理的现有形式。尽管有严格的透明度和问责标准，但理论与现实之间仍存在差距。本文讨论了当前用于大语言模型评估、机器学习工作流程和应用性能监测的平台的固有缺陷。现有脱节的解决方案无法保护无边界状态空间智能体架构免受对齐漂移、SaaS安全问题和影子人工智能系统未经授权部署等严重威胁。为此提出了一种解决方案，即基于OpenTelemetry基础设施平台构建一个连贯的多层次人工智能治理堆栈Traccia。Traccia通过将遥测数据、被动语义护栏评估和执行谱系添加到哈希跟踪账本中，解决了人工智能对齐的最后一英里问题。它通过附加防篡改指纹和SHA-256内容哈希自动创建合规证据包，映射到监管要求且不侵犯数据隐私。通过系统评估，为企业范围的自主人工智能系统管理创建了坚实的机器可读基础。
英文摘要
The rapid development of Large Language Models (LLMs) and Artificial Intelligent (AI) powered autonomous agents has fundamentally changed the existing forms of software governance. In spite of the rigorous standards of transparency and account ability required according to the international frameworks such as the European Union's AI Act, there is a considerable gap between theory and reality. The present study discusses the inherent drawbacks of currently utilized platforms for LLM evaluation, machine learning workflow, and application performance monitoring in general. It has been shown that current disjointed solutions fail to protect unbound state space agentic architecture from serious threats such as alignment drift, SaaS security concerns, and unauthorized deployment of shadow AI systems. Moreover, a solution is proposed for overcoming the discussed challenges in form of a coherent multi-level AI governance stack Traccia built on the top of OpenTelemetry infrastructure platform. Traccia resolves the last mile for AI Alignment by adding the telemetry data, passive semantic guardrail assessment, and execution lineage into a hashed trace ledger. Traccia automatically creates compliance evidence packages by appending tamper-resistant fingerprints and SHA-256 content hash, that map to regulatory requirements (Articles 12, 14, 19, 26(6), and 50 of the EU AI Act) without invading any data privacy. By performing this evaluation in a methodical manner, a solid machine-readable base has been created for enterprise-wide management of autonomous AI systems.
Comments26 pages, 2 figures, 3 tables, Declaration of generative AI and AI-assisted technologies in the writing process, Declaration of competing interest
