Audit-Ready Compliance Platform Architecture for Large Language Model Regulated Enterprises.

Abstract

Generative language technology has entered regulated enterprises faster than the controls built to govern it, creating a widening gap between capability and defensibility. The probabilistic and opaque behavior of such systems sits uneasily beside supervisory regimes that expect deterministic, explainable, and reconstructable decisions. This article sets out an architecture that treats verifiability as a founding property rather than a later addition so that a regulated deployment can prove its own trustworthiness at any moment. The design begins from the obligations that bind operators, distills them into a small set of anchoring principles, and states plainly the threats it withstands and the assumptions on which its guarantees rest. It organizes the platform into cooperating layers for data governance, model lifecycle, and inference, then surrounds them with pre-deployment validation, continuous evidence capture, explanation of individual decisions, disciplined human oversight, and a retrieval workflow that turns supervisory questions into queries over standing evidence. Provenance, immutability, reproducibility, confidentiality, and accountability run through every layer, binding each recorded action to an identifiable actor and an authorizing policy. The result reframes readiness as a continuous background condition rather than an emergency response, letting an enterprise move quickly while remaining answerable. Honest attention to residual constraints, from imperfect drift detection to the tension between privacy and utility, strengthens the posture by making the boundaries of each guarantee legible to those who must rely on it.