The designation of election infrastructure as a critical infrastructure subsector by the U.S. Department of Homeland Security in 2017 highlights the ongoing need to safeguard these systems from emerging cyber, physical, and insider threats. As complex socio-technical systems, election infrastructure relies on the interaction between hardware, software, and human operators, making it vulnerable to a range of security risks. This study builds upon prior security assessments conducted by the U.S. Elections Assistance Commission of precinct count optical scanners (PCOS), the primary machines used for ballot scanning and tabulation. To do so, this work employs Software Failure Modes and Effects Analysis (SFMEA), a widely used method for identifying and mitigating software-related failures. Specifically, through an extensive literature review and structured application of SFMEA, 60 additional threats were identified and incorporated into an updated threat tree model. By integrating SFMEA, which takes a bottom-up approach to trace potential failure points, with threat tree analysis, a top-down method for identifying root causes, this research adapts a more comprehensive, bi-directional risk evaluation framework. The results enhance election system security by demonstrating how SFMEA can be systematically applied to strengthen threat assessments. Furthermore, the methodology demonstrates a systematic threat and mitigation analysis approach to address the cyber, physical, and insider risks, including those posed by adversaries and trusted insiders, that is also applicable to national critical infrastructure socio-technical systems and processes.