Designing Secure Generative AI for Global Supply Chains Governance-by-Design for Trustworthy Deployment
Year: 2026
Type: conference-paper
Abstract: Generative artificial intelligence (GenAI)—particularly large language models (LLMs), multimodal systems, and retrieval-augmented generation (RAG) pipelines—is rapidly reshaping global supply chain management (SCM). When properly architected, GenAI can improve forecasting, automate document processing, strengthen threat detection, and enable adaptive decision support across logistics networks. However, these benefits come with security, privacy, and governance challenges that are especially salient in cross-border logistics where data residency, regulatory heterogeneity, and adversarial actors complicate deployment. This paper synthesizes emerging GenAI capabilities and maps them to concrete, security-sensitive SCM applications. We propose secure architectural patterns (on-premises/hybrid RAG, federated fine-tuning, verifiable logging), operational controls (data minimization, provenance tagging, human-in-the-loop gates), and governance measures (model risk assessment, incident playbooks). Through illustrative case vignettes and a future research agenda, the paper aims to guide practitioners and researchers toward deploying generative AI in global supply chains in ways that maximize operational value while minimizing systemic risk.
Authors: Paul C. Hong, Young B. Choi, David W. Hwang
Institutions: University of Toledo, Regent University, Shippensburg University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7202380852