Beyond the Air Gap: Autonomous AI-Driven Anomaly Detection in Converged IT/OT Critical Infrastructure Environments

Abstract

The convergence between Information Technology (IT) and Operational Technology (OT) has occurred at an extremely rapid pace and has made traditional air-gapped security models ineffective in securing critical infrastructure. With growing usage of AI-enhanced capabilities to conduct automated reconnaissance, zero-day code exploitation, and rapid lateral movement, human-controlled surveillance within the industrial setting is no longer able to react as fast as it can. In this article, an independent, AI-assisted, anomaly detecting system is suggested that can operate within machine speed and provide real-time protection against pending cyber-physical attacks.
The method combines self-learning models, behavioral baselining, cross-domain telemetry, and simulation on a digital-twin to detect subtle and newly emerging anomalies in highly complex industrial ecosystems. The system improves situational awareness by correlating IT and OT signals and helps decrease the operator load and provide quick mitigation without affecting safety. The results indicate that the switch to machine-vs-machine defense model is posed as the prerequisite requirement to ensure the safety of modern critical infrastructure.