The rapid evolution of risk landscapes in supply chains necessitates innovative management tools that enhance decision-making and process efficiency. Building on our previously proposed framework, this paper introduces a software prototype named LARD-SC (LLMs for Automated Risk Detection in Supply Chains). It utilizes Large Language Models (LLMs) for automated risk identification and assessment of risks impacting on global supply chain and integrates the Cambridge Taxonomy of Business Risks (CTBR) to categorize risk events. Additionally, it employs Neo4j, a graph database, for dynamic risk visualization. This integrated approach aims to upgrade traditional Supply Chain Risk Management (SCRM) processes by providing supply chain professionals with an intuitive, interactive tool to manage risks more effectively, thereby increasing resilience and adaptability in complex supply environments. A case study testing the functionality and impact of the prototype in a real-world scenario with Apple Inc. demonstrates its efficacy, showing a significant improvement in risk detection and management efficiency.