# Embedding Security Awareness in IoT Systems: A Framework for Providing Change Impact Insights
## 2 Background
Their framework identifies four core dimensions of access control--subject, object, environment, and behavior--and incorporates a risk assessment mechanism based on prior knowledge of potential threats and their severity. The system features a continuous trust evaluation process and a dynamic access control model that performs fine-grained, session-based access decisions, guided by situational awareness of potential risks. However, its reliance on a static, predefined threat model limits its adaptability to unforeseen changes and hinders its ability to fully capture the implications of dynamic, real-world threats in complex healthcare environments. However, the inherent complexity of IoT ecosystems--combined with the vast volume of heterogeneous and multimodal data--poses significant challenges to achieving effective _security awareness_. Extracting actionable insights from raw data demands advanced analytical capabilities. Machine learning (ML) and deep learning (DL) techniques have demonstrated considerable success in tasks such as threat prediction and anomaly detection within IoT environments [17; 18]. Despite their effectiveness, these models often operate as \"black boxes\", which limits their applicability in security-critical domains where trust, transparency, and accountability are paramount. Explainable AI (XAI) seeks to address this limitation by enhancing the interpretability of ML/DL models, providing insights into which input features influence predictions and why [19; 20]. XAI methods are generally categorized into model-specific and model-agnostic approaches. Model-specific techniques are tailored to particular types of models that are inherently more transparent, such as decision trees or linear models [39]. In contrast, model-agnostic methods can be applied to any black-box model regardless of its internal architecture, making them suitable for interpreting complex models like deep neural network (DNN). These methods typically rely on surrogate models to approximate and explain the behavior of the original black-box models [40]. For instance, Local Interpretable Model-Agnostic Explanations (LIME) [41; 42; 43] constructs simple, locally faithful surrogate models that mimic the behavior of the complex model around a specific instance. Similarly, SHapley Additive exPlanations (SHAP) [44; 45], grounded in cooperative game theory, assigns importance to input features by calculating their marginal contributions to a model's prediction. Beyond merely justifying predictions, XAI can reveal hidden data patterns and offer deeper contextual insights [46]. These insights are critical, as mitigation strategies based on opaque or misunderstood model behavior may inadvertently introduce new vulnerabilities [6; 47]. Despite its promise, effectively integrating XAI into autonomous IoT systems remains a significant challenge. The interpretation of XAI outputs often requires human expertise, limiting opportunities for automation and scalability. To truly embed _security awareness_ into autonomous IoT environments, there is a need for an automated mechanism that can extract, interpret, and map XAI-derived insights to the system's security profile. This would enable the identification of affected security requirements, components, or functionalities and support adaptive security decision-making [6]. While advances in XAI have enhanced interpretability, a critical gap remains in contextualizing these insights with respect to a system's security profile. A security profile defines the system's security requirements and abstracts the interrelationships among those requirements, along with the corresponding security mechanisms and system functionalities designed to fulfill them [16]. To systematically represent this profile, a structured methodology is required. Security Assurance Cases (SACs) [16; 21], typically modeled using Goal Structuring Notation (GSN) [22], provide a formal framework for articulating and evaluating a system's security assurance.