Beyond Detection: A Hallucination Governance Architecture for AI-Verified Professional Communication

As large language models become integrated into professional communication workflows, the reliability of AI-generated content emerges as a governance challenge rather than solely a technical one. This paper proposes a hallucination governance architecture, a structured approach to managing AI-generated content reliability through systematic verification processes, source classification, adversarial quality testing, and operational governance controls. Drawing on accountability theory, the epistemology of testimony, and information governance literature, the architecture addresses a documented gap: existing approaches to hallucination primarily target detection and mitigation at the model level, while organisational governance of AI-generated content in professional contexts remains largely unaddressed. The architecture comprises five integrated components: (1) a calibrated four-tier source hierarchy with explicit verification markers enabling human validation; (2) an adversarial quality gate with quantified risk scoring; (3) a dual-mode execution system supporting both rapid and comprehensive verification; (4) channel-agnostic governance with domain-specific templates; and (5) cross-platform deployment capability. The Grounded Gate Protocol (GGP) demonstrates one feasible instantiation, implemented as a prompt-engineering framework deployable across major LLM platforms. Developed through Design Science Research methodology with iterative refinement across professional use cases, the architecture is presented as a conceptual contribution requiring empirical validation. This work invites the research community to evaluate, critique, extend, and empirically test this governance approach.