Strategic Adoption of Generative AI in U.S. Manufacturing: Balancing Innovation Acceleration with Trade, Compliance, and Intellectual Property Governance

The integration of generative AI into U.S. manufacturing environments introduces both innovation potential and regulatory complexity. This paper proposes a compliance-aware architecture for deploying generative AI models within industrial design and production workflows. The framework defines technical control layers, including model access governance, embedded compliance screening, and data-classification mechanisms to prevent model leakage, safeguard intellectual property, and ensure adherence to U.S. export control regulations. Unlike prior studies focused solely on AI performance or policy analysis, this work outlines a system-level implementation model combining generative design automation, secure model orchestration, and risk-aware data pipelines. Through scenario-based modeling and workflow analysis, the study demonstrates how manufacturers can accelerate innovation while maintaining governance integrity across digital supply chains. The proposed architecture bridges AI engineering, cybersecurity, and regulatory design, enabling responsible adoption of generative AI in complex manufacturing ecosystems.