Machine-to-machine (M2M) communication has emerged as the operational backbone of Industrial Internet of Things (IIoT) and smart manufacturing ecosystems, enabling autonomous data exchange, real-time analytics, and cyber-physical coordination across distributed platforms. However, the expansion of intelligent factory networks and interconnected industrial devices has created new vulnerabilities in database infrastructures that store and manage high-value operational data. This study examines database-level security challenges inherent in M2M communication environments and analyzes how factors such as heterogenous device authentication, large-scale data flows, weak encryption implementations, and high-velocity industrial workloads contribute to cyber risks. Using a mixed-method research design consisting of dataset modeling, structural analysis, and performance-driven simulations, the study identifies critical exposure points and evaluates mitigation mechanisms. Results highlight the need for adaptive access control, anomaly-driven monitoring, secure data replication, and tamper-resistant architectures. The findings contribute to enhancing the security posture of IIoT-based manufacturing systems and strengthening industrial cyber resilience against evolving threats.

Industrial IoT; Machine-to-Machine Communication; Database Security; Smart Manufacturing; Access Control; Data Integrity; Cyber-Physical Systems; Threat Mitigation.