## 9 Conclusions  
This security analysis of an ACN system highlights the importance of robust cybersecurity measures within the automotive industry, particularly regarding functions within a car and the automatic notification system for crash incidents. As the automotive landscape rapidly evolves with the integration of advanced technologies, the potential benefits of such a system are undeniable: rapid and accurate crash detection, coupled with the immediate deployment of emergency response teams, holds the promise of saving countless lives. The interconnected nature of modern vehicles and their reliance on digital infrastructure make them susceptible to a number of cyber threats. Malicious actors could exploit vulnerabilities in the system to not only manipulate critical functions of the vehicle but also compromise the privacy and safety of vehicle occupants. To manage the cybersecurity risks, all product life-cycle phases must be covered: concept, development, production, operation, and maintenance. The usage of ISO/SAE 21434 provided a valuable tool for assessing the security of automotive architecture, paving the way for its usage on multiple systems, and possibly detecting all the risks of a given architecture. An average of 310 threats were detected using the TARA methodology in combination with ISO/SAE 21434, demonstrating its applicability also to individual architectures. Furthermore, the privacy concerns of an ACN system cannot be overlooked. As demonstrated by the study, personal data such as pictures of the user and continuous tracking using GPS can lead to critical threats from the information disclosure and snooping point of view. Handling personal data, including location and health information, demands a balance between emergency response efficacy and individual privacy. This balance requires transparent data usage policies, explicit user consent, and stringent access controls to prevent unauthorized use or abuse of sensitive information. Therefore, deeper analysis must be conducted on the following topics in order to improve and extend the current work:  
1. **User privacy**: One of the first steps is to expand the scope of our analysis to consider how the system handles user data. This includes examining the practices for collecting, storing, and using data. It is important to look at how long data are retained and the potential for unauthorized access to these data. Decentralization solutions are expanding and can be provided by moving the data collected in the automotive context away from centralized servers, leveraging techniques related to user-centric data spaces. 2. **Ethical considerations**: It is not just about technology; there are ethical dimensions to securing the ACN system. This involves diving deep into the moral and societal consequences of our actions. We must consider the safety of users, their privacy rights, and our responsibility as system developers. Establishing ethical disclosure practices for vulnerabilities is critical to maintaining trust and transparency. Modifications to Equation (1) can be applied in order to add the ethical impact (EI) of the considered feature. 3. **Autonomous system and regulatory compliance**: We also need to ensure that our security measures align with legal and regulatory frameworks related to autonomous systems. This involves researching and understanding the specific rules and standards that apply to automated systems, which have always been characterized by privacy issues, such as in the case of GDPR. Compliance not only keeps us within the bounds of the law but also enhances security. 4. **Risk value determination**: The parameters considered in Equation (1) consider the same weight for all the impacts (safety, financial, operational and privacy), but in some cases this is not completely correct.