Core Viewpoint - Bosch has introduced an integrated Failure and Threat Mode and Effect Analysis (FTMEA) framework to address the increasing challenges in functional safety and cybersecurity within the automotive industry, emphasizing the need for a unified approach to analyze interdependencies between safety-related failures and cybersecurity threats [4][46]. Group 1: Background and Problem Statement - The automotive industry faces growing complexity in semiconductor devices, necessitating a paradigm shift in reliability engineering to ensure functional safety (FuSa) and cybersecurity [7]. - Traditional analysis methods, such as Failure Mode and Effects Analysis (FMEA), often treat functional safety and cybersecurity independently, leading to fragmented analyses and potential oversight of vulnerabilities [7][8]. - There is a significant gap in providing a quantitative, traceable mechanism to model the interdependencies between functional failures and cybersecurity threats [8]. Group 2: Key Contributions - The FTMEA framework introduces a novel set of Cross-Domain Correlation Factors (CDCF) to quantitatively assess the interdependencies between functional safety failure modes and cybersecurity threat modes [10]. - An enhanced Risk Priority Number (RPN) calculation method is proposed, integrating CDCF into the assessment of occurrence and detection rates, resulting in more accurate risk prioritization [10][11]. - The framework provides a clear operational methodology for integrating safety and cybersecurity considerations throughout the analysis lifecycle, from hazard identification to mitigation strategy evaluation [11]. Group 3: Case Study and Practical Application - A detailed case study on an automotive-specific Application-Specific Integrated Circuit (ASIC) configuration register demonstrates the practical application of the FTMEA framework, revealing previously obscured cross-domain risks and improving the effectiveness of mitigation strategies [11][37]. - The case study highlights the importance of quantifying CDCF values and provides a comparative analysis with benchmark FMEA/TARA, showcasing the framework's ability to enhance risk assessment and resource allocation [11][43]. Group 4: Future Directions - Future work will focus on applying the FTMEA framework to complex use cases, improving the measurement of correlation factors, and exploring the integration of machine learning and artificial intelligence to automate the derivation of relevant factors [47].