# Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scale arXiv: https://arxiv.org/abs/2603.03340 ยท HTML: https://mlsanity.com/paper/2603.03340/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. - Builds on 37 of its 142 references that are in the map. - First posted on arXiv 15 Feb 2026. ## Placed in - LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/) ## Builds on (most cited first, 30 of 37) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023) - [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023) - [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023) - [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023) - [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020) - [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021) - [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020) - [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022) - [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023) - [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019) - [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023) - [Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem](https://mlsanity.com/paper/2310.02521/) (2023) - [Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling](https://mlsanity.com/paper/2402.17861/) (2024) - [Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics](https://mlsanity.com/paper/2202.08792/) (2022) - [Ethics-Based Auditing to Develop Trustworthy AI](https://mlsanity.com/paper/2105.00002/) (2021) - [ChatGPT: More than a Weapon of Mass Deception, Ethical challenges and responses from the Human-Centered Artificial Intelligence (HCAI) perspective](https://mlsanity.com/paper/2304.11215/) (2023) - [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022) - [Ethics as a service: a pragmatic operationalisation of AI Ethics](https://mlsanity.com/paper/2102.09364/) (2021) - [Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance](https://mlsanity.com/paper/2402.01096/) (2024) - [Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns](https://mlsanity.com/paper/2111.09478/) (2021) - [Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges](https://mlsanity.com/paper/2201.04736/) (2022) - [Towards a Roadmap on Software Engineering for Responsible AI](https://mlsanity.com/paper/2203.08594/) (2022) - [Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Study](https://mlsanity.com/paper/2301.11616/) (2023) - [A Framework for Assurance Audits of Algorithmic Systems](https://mlsanity.com/paper/2401.14908/) (2024) - [Putting AI Ethics into Practice: The Hourglass Model of Organizational AI Governance](https://mlsanity.com/paper/2206.00335/) (2022) - [Monitoring Misuse for Accountable 'Artificial Intelligence as a Service'](https://mlsanity.com/paper/2001.09723/) (2020) - [Audit and Assurance of AI Algorithms: A framework to ensure ethical algorithmic practices in Artificial Intelligence](https://mlsanity.com/paper/2107.14046/) (2021) ## Built on by (most cited first, 1 of 1) - [From Agent Output to Authorized Transition](https://mlsanity.com/paper/2609.28216/) (2026) ## Abstract (arXiv) 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.