Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scale

Traditional audits cannot keep pace with AI systems: The paper identifies a critical "assurance scalability crisis" where point-in-time, document-based audits fail to monitor autonomous AI agents that exhibit non-deterministic behavior and operate across distributed, rapidly evolving environments—meaning current compliance methods are fundamentally outdated for modern AI deployments.

Trustworthiness should be continuous, not a one-time certification: Drawing parallels to cybersecurity posture management, the framework reframes trustworthiness as an ongoing signal rather than a static certificate, reflecting the reality that AI systems and regulatory requirements change faster than traditional audit cycles can accommodate.

Existing frameworks are fragmented and manual: The paper's benchmark analysis of thirteen leading trustworthy AI frameworks reveals a "posture readiness gap," indicating that current assurance approaches remain largely disconnected from each other and require manual effort, creating scalability problems both within organizations (vertical scale) and across complex, heterogeneous systems (horizontal scale).

A new framework separates policy from execution: TAIP operationalizes the NIST AI RMF's Test, Evaluate, Verify, Validate cycle through reusable "AI Assurance Objects" that decouple what organizations must do (policy content) from how they do it (execution), enabling automated, composable assurance that can work across different jurisdictions and AI systems.

Risk-based regulation demands demonstrable ongoing control: Organizations now face regulatory requirements to continuously prove their AI governance is adequate and effective, not just document it once—a shift that existing fragmented, manual frameworks cannot support at scale.