# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 7 Results and Discussion
Addressing these specific limitations that lead to Medium or Low (rather than Negligible) residual risks points towards the necessity for continuous improvement and potentially the adoption of the more advanced security paradigms discussed earlier Section 6.3. Techniques such as AI-driven adaptive IDS could offer better detection of sophisticated DoS or Tampering attacks that evade signature-based systems. Likewise, blockchain technology could provide stronger guarantees for data integrity and non-Repudiation, potentially mitigating residual risks associated with lightweight digital signatures or complex logging requirements, especially for sensitive sensor data (CS). Therefore, while Table 4 presents the outcome of applying the core set of security controls analyzed within this TARA cycle, the accompanying rationale underscores that achieving even lower residual risk levels likely requires exploring and validating these more advanced, context-aware security solutions in future development iterations. In response to reviewer suggestions, we emphasize the need for advanced threat detection mechanisms beyond static controls. AI-driven, adaptive intrusion detection systems (IDSs) are capable of learning new threat patterns and outperforming signature-based IDS in detecting evolving DoS or Tampering attacks. Such systems also support dynamic policy enforcement and anomaly classification tailored to driving contexts. DMS systems increasingly interact with V2X networks, which introduce broader cybersecurity exposure. However, this connectivity also enables cooperative detection, such as shared anomaly alerts between vehicles and infrastructure nodes. To secure this extended perimeter, decentralized key management schemes, such as blockchain-based trust anchors and threshold cryptography, can offer tamper-proof audit trails and enhance the verifiability of system logs. Recent studies propose blockchain-enhanced logging systems that ensure data integrity and non-Repudiation while maintaining processing efficiency through lightweight consensus mechanisms. Furthermore, smart contract integration can facilitate automated key revocation and trust policy enforcement across multi-domain vehicular ecosystems. **Data privacy and GDPR compliance. ** A critical dimension of modern DMS deployment lies in safeguarding user privacy. The biometric and behavioral data collected demand rigorous adherence to GDPR. This includes the following:  
* **Data minimization (Art. 5):** Employing edge-based processing architectures (e.g., containerized analytics or federated learning) that avoid raw data transmission;
* **Right to erasure (Art. 17):** Implementing off-chain anonymized indexing and user-controlled data lifecycles to support selective deletion;
* **Consent management:** Using verifiable digital consent records tied to cryptographically secure identities. To strengthen compliance and transparency, we recommend that future DMS implementations adopt Data and Impact Privacy Integrated Assessments (DIPIA), which combine security threat modeling and legal risk assessments from the earliest design phases. This analysis confirms that the evaluated DMS architecture is effective in reducing security risks through established mitigation practices. However, achieving higher assurance levels and aligning with modern legal standards necessitates the integration of adaptive AI, secure V2X protocols, privacy-enhancing technologies, and decentralized trust infrastructures. This holistic approach responds directly to reviewer recommendations and reflects the broader direction of intelligent and ethically aligned vehicular cybersecurity.