A Trustworthy AI and Data Governance Architecture for Ensuring Integrity and Ethics in Large Language Model Deployments across Enterprise Platforms
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".