Foundation Model–Enhanced Semantic Risk Interpretation for Human–Device Identity Interactions

Abstract

The high rate of interconnected device and intelligent system proliferation has greatly enhanced the complexity of human - device identity interactions and this has greatly complicated the existing identity and access management mechanisms. Traditional methods of risk assessment are mostly based on fixed rules or statistical outlier measuring, that do not possess semantic knowledge and are unable to accommodate changing behavioural and contextual differences. This paper outlines a foundation model-enhanced semantic risk interpretation framework to overcome these drawbacks in human-device interactions of identity. The suggested method is based on the representational and reasoning power of foundation models through interpreting identity risk as a semantic construct based on the signals of human behaviour, attributes of device trust, and contextual information. The framework allows context-sensitive, interpretable risk thinking that produces interpretations and decisions based on context, unlike binary or score-based decisions, which are not in line with the dynamism of contemporary digital landscapes. The paper demonstrates a well defined conceptual architecture, semantic risk modelling process and theoretical discussion on the benefits of semantic AI-based reasoning as compared to conventional and machine learning-based identity risk assessment model. The suggested framework will advance the identity security research by proposing a human-centred, explainable, and adaptive risk interpretation paradigm, and which provides real-world tipping points to next-generation secure systems in enterprise, IoT, and smart settings.