The research reported here investigates insider threats in supply chain contexts by simulating the behavioural trajectory of a disgruntled employee using an adaptive network model. It introduces a scenario grounded in the real-life case of Kandula Nagaraju, illustrating how organisational change and psychological disengagement can trigger malicious actions. Using network-oriented modelling, the system captures interlinked state variables representing cognitive states, organisational responses, and escalation dynamics. The model’s architecture is structured in a multilevel manner and includes adaptive feedback mechanisms. A baseline simulation demonstrates how undetected disengagement can escalate into data theft, highlighting vulnerabilities in static organisational systems. What-if analyses manipulate detection speed, access misuse thresholds, and learning efficiency, showing that faster detection and organisational adaptability significantly reduce risk. The discussion emphasises the importance of feedback loops, particularly between detection and learning, in disrupting threat escalation cycles. Finally, the study offers practical recommendations and proposes future research to refine some of the assumptions and enhance insider threat prevention strategies.