A Trustworthy AI and Data Governance Architecture for Ensuring Integrity and Ethics in Large Language Model Deployments across Enterprise Platforms

Abstract

The rapid enterprise adoption of Large Language Models (LLMs) has transformed knowledge work, decision support, and digital service delivery, yet their deployment introduces substantial risks related to data integrity, privacy exposure, algorithmic bias, hallucinated outputs, and unclear accountability across organizational boundaries. Traditional data governance and information security models were not designed to manage probabilistic, generative systems whose behaviors evolve dynamically with context, prompting the need for a new class of trustworthy AI governance mechanisms. This paper proposes a comprehensive Trustworthy AI and Data Governance Architecture that embeds integrity, ethics, and compliance controls directly into the lifecycle of enterprise LLM deployments, spanning data acquisition, model training, fine-tuning, retrieval augmentation, prompt orchestration, inference monitoring, and post-deployment auditing. The framework integrates policy-driven guardrails, lineage tracking, explainability checkpoints, human-in-the-loop oversight, and continuous risk evaluation to ensure traceability and responsible decision making. Control layers are aligned with emerging guidance from National Institute of Standards and Technology risk management principles and regulatory expectations established by the European Commission AI governance initiatives, enabling measurable assurance of fairness, reliability, and accountability. Through a design-science methodology and enterprise reference scenarios, the architecture demonstrates how organizations can operationalize ethical AI practices without sacrificing scalability or performance. The proposed model advances governance-by-design, transforming trust from an abstract principle into enforceable technical and procedural controls, thereby enabling safe, compliant, and sustainable LLM adoption across complex enterprise platforms.