可信AI姿态（TAIP）：一种用于在横向和纵向规模上持续确保代理系统AI可信度的框架
Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scale
AI总结：
TAIP框架通过将可信度定义为持续生成的信号，提供了一种在横向和纵向尺度上持续确保代理系统AI可信度的方法，结合本体模型和证据门限基准，实现可重用的AI保证对象。
摘要：
自主、高速的代理AI系统出现，正在创造内部保证可扩展性危机。基于时间点的文档审计无法跟上代理在快速演变环境中非确定性行为和分布式部署的速度。这种危机是双尺度的：在垂直方向上，治理和控制义务变化速度比框架能执行的速度快；在水平方向上，保证机制无法在复杂、异构的系统和证据源之间扩展。基于风险的监管现在要求组织证明持续的控制充分性和有效性，但现有的可信AI保证和审计框架仍然碎片化且大多手动。借鉴网络安全姿态管理的演变，本文将可信度重新定义为持续生成的信号，而不是静态证书。本文贡献包括：1）一个可信AI保证本体，描述从监管义务到可验证证据的端到端路径；2）一个基于本体的证据门限基准，评估十三个领先框架，揭示姿态准备差距；3）可信AI姿态（TAIP）框架，将NIST AI RMF测试、评估、验证、验证（TEVV）周期作为可重用的AI保证对象进行操作化。TAIP将政策内容（‘是什么’）与执行语义（‘如何’）分离，使在不同司法管辖区和代理系统之间可组合、可自动化的保证成为可能。来自异构工具的证据被标准化并递归聚合到声明、系统、组织和生态系统层面的姿态。一个将澳大利亚AI护栏映射到Microsoft 365 Copilot的用例展示了声明分解、证据绑定和姿态计算的实际应用。通过标准化执行同时允许政策变化，TAIP实现了可扩展的、机器速度的可信信号生成。
英文摘要：
The emergence of autonomous, high-velocity Agentic AI systems is creating an internal assurance scalability crisis. Point-in-time, document-based audits cannot keep pace with non deterministic behaviour and distributed deployments of agents across rapidly evolving environments. The crisis is dual-scale: vertically, governance and control obligations change faster than frameworks can operationalise them; horizontally, assurance mechanisms fail to scale across complex, heterogeneous systems and evidence sources. Risk-based regulation now requires organisations to demonstrate ongoing control adequacy and effectiveness, yet existing Trustworthy AI Assurance and Audit frameworks remain fragmented and largely manual. Drawing on the evolution of cybersecurity posture management, this paper reframes trustworthiness as a continuously generated signal rather than a static certificate. It contributes 1) A Trustworthy AI Assurance Ontology modelling the end-to-end pathway from regulatory obligation to verifiable evidence 2) An ontology-driven, evidence-gated benchmark of thirteen leading frameworks, revealing a posture readiness gap 3) The Trustworthy AI Posture (TAIP) framework, which operationalises the NIST AI RMF Test,Evaluate,Verify,Validate (TEVV) cycle as reusable AI Assurance Objects. TAIP decouples policy content ('what') from execution semantics ('how'), enabling composable, automatable assurance across jurisdictions and agentic systems. Evidence from heterogeneous tools is normalised and recursively aggregated into posture at claim, system, organisational, and ecosystem levels. A use case mapping Australian AI Guardrails to Microsoft 365 Copilot demonstrates claim decomposition, evidence binding, and posture computation in practice. By standardising execution while allowing policy variation, TAIP enables scalable, machine-speed trust signal generation.
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。
