Implementing Autonomous Digital Factories in the U.S.: Cyber-Physical Security, Regulatory Constraints, and Competitive Strategy

The transition toward autonomous digital factories represents a critical evolution in advanced manufacturing, driven by the integration of cyber-physical systems (CPS), industrial Internet of Things (IIoT) infrastructures, robotics, and artificial intelligence–based decision automation. However, increasing connectivity between operational technology (OT) and information technology (IT) domains introduces expanded cyber-physical attack surfaces, particularly within industrial control system (ICS) environments. This paper examines the deployment of autonomous digital factories in the United States with attention to cyber-physical security challenges, regulatory compliance constraints, and competitive performance requirements. A layered system architecture for autonomous manufacturing operations is presented, followed by a detailed threat analysis of ICS networks. To address the limitations of signature-based industrial intrusion detection, the study proposes an AI-driven anomaly detection framework that leverages machine operation telemetry, PLC command sequence profiling, and network flow behavior modeling to identify deviations indicative of cyber-physical intrusions. The proposed approach aligns with U.S. industrial security standards, including NIST SP 800-82 and ISO/IEC 62443, contributing a scalable and adaptive defense capability for secure Industry 4.0 implementation.