# Embedding Security Awareness in IoT Systems: A Framework for Providing Change Impact Insights
## 6 Conclusions  
The rapid integration of IoT technologies holds transformative potential to enhance productivity, sustainability, and resilience across various domains. However, realizing these benefits presents significant challenges--particularly in managing the complexity, dynamism, and security sensitivity inherent in such environments. This paper addresses these challenges by proposing a novel security-aware framework for IoT systems. By integrating contextual intelligence, explainable artificial intelligence (XAI), and structured security assurance modeling, the framework enables real-time reasoning about environmental and operational changes and their implications for system security. A key innovation of this study is the introduction of a graph-based procedural approach to quantify the importance of individual system components. This enhances the system's ability to prioritize and adapt its security responses. As a proof of concept, we develop a prototype demonstrating that XAI outcomes can be effectively leveraged to extract insights on dynamic changes, and we introduce a methodology to map those outcomes to the system's security profile, ensuring that the resulting insights align with established security objectives. To support this, we generate a call graph based on the execution trace of process invocations. Our current testbed includes only 15 processes, so collecting execution traces and updating the call graph at regular intervals introduces minimal overhead. However, in larger and more complex deployments--such as those in industrial IoT, healthcare, smart cities, smart grids, or real-world smart agriculture systems--this approach could introduce notable resource overhead. Furthermore, due to the distributed and decentralized nature of many IoT architectures, collecting execution traces can be difficult or incomplete. In future work, we will explore alternative strategies for constructing process interaction graphs that are scalable and resilient to these challenges. Another limitation lies in the framework's reliance on a well-maintained and complete security profile, represented using Structured Assurance Cases (SACs). At present, this profile is static, and any changes to the system architecture must be manually updated in the SAC. Although a framework from [16] includes three operators that allow limited dynamic evolution of the SAC--such as replacing contextual values or modifying existing functionality--these operators are not sufficient to fully support architectural changes, such as adding or removing components or introducing new functionalities. Thus, there is a clear need to develop methodologies that enable the dynamic evolution of SACs in response to system-level changes. However, addressing this challenge is beyond the scope of this research. In summary, this work lays the foundation for building intelligent, secure, and trustworthy IoT infrastructures by synergistically combining interpretability, adaptability, and risk-awareness. We acknowledge that the current implementation of the security awareness component introduces some performance overhead, highlighting the need for further optimization of computational processes. Additionally, the framework relies on XAI methods to produce actionable insights, which must be rigorously evaluated to ensure their fidelity, reliability, and practical utility. Future work will focus on optimizing performance and conducting thorough evaluations of the employed XAI techniques. Additionally, we recognize that the current centralized implementation may not align well with the inherently decentralized nature of IoT architectures. Addressing this limitation, along with incorporating mechanisms for data privacy protection, will be a key focus of our future efforts. To the best of our knowledge, no existing approach directly maps XAI outcomes to system security profiles for generating insights about uncertain changes and their impact on the system's security posture. Therefore, direct comparisons with existing methods are not feasible at this stage.