AI Trust OS -- A Continuous Governance Framework for Autonomous AI Observability and Zero-Trust Compliance in Enterprise Environments

Abstract

The accelerating adoption of large language models, retrieval-augmented generation pipelines, and multi-agent AI workflows has created a structural governance crisis. Organizations cannot govern what they cannot see, and existing compliance methodologies built for deterministic web applications provide no mechanism for discovering or continuously validating AI systems that emerge across engineering teams without formal oversight. The result is a widening trust gap between what regulators demand as proof of AI governance maturity and what organizations can demonstrate. This paper proposes AI Trust OS, a governance architecture for continuous, autonomous AI observability and zero-trust compliance. AI Trust OS reconceptualizes compliance as an always-on, telemetry-driven operating layer in which AI systems are discovered through observability signals, control assertions are collected by automated probes, and trust artifacts are synthesized continuously. The framework rests on four principles: proactive discovery, telemetry evidence over manual attestation, continuous posture over point-in-time audit, and architecture-backed proof over policy-document trust. The framework operates through a zero-trust telemetry boundary in which ephemeral read-only probes validate structural metadata without ingressing source code or payload-level PII. An AI Observability Extractor Agent scans LangSmith and Datadog LLM telemetry, automatically registering undocumented AI systems and shifting governance from organizational self-report to empirical machine observation. Evaluated across ISO 42001, the EU AI Act, SOC 2, GDPR, and HIPAA, the paper argues that telemetry-first AI governance represents a categorical architectural shift in how enterprise trust is produced and demonstrated.

Figures (2)

• Figure 1: AI Trust OS four-layer conceptual governance architecture. Layer 1 enforces a zero-trust telemetry boundary through ephemeral read-only probes against external AI infrastructure. Layer 2 houses the core governance modules, including Shadow AI discovery, the AI System Registry, red teaming, and privacy mapping. Layer 3 synthesises machine-collected evidence into predictive intelligence and LLM-generated compliance documentation. Layer 4 exposes continuously maintained governance outputs to regulatory frameworks, auditors, and enterprise buyers. The dashed boundary denotes external AI infrastructure that is observed but never ingested.
• Figure 2: AI Trust OS deployment and implementation topology. The user layer communicates with the Vercel edge runtime through HTTPS, where Clerk handles authentication and API routes manage governance workflows. An encrypted credential vault supplies ephemeral credentials to BullMQ-orchestrated probe workers hosted on Render, which execute read-only metadata checks against external AI infrastructure, including LangSmith, Datadog LLM, AWS, and model provider APIs llm-observabilitylangsmith2023. Probe results are persisted as control assertions in a Neon PostgreSQL evidence ledger via Prisma ORM, with all queries partitioned by workspace identifier to enforce tenant isolation. The LLM synthesis pipeline consumes passed assertions from the evidence ledger — never raw payloads or PII — and generates board-grade compliance documentation through a stateless GPT-4o-mini pipeline.