Securing the Software Supply Chain: Advanced Strategies for Mitigating Third-Party Vulnerabilities within the Software as a Service (SaaS) Ecosystem

Third-party dependency is increasingly shifting from being a business advantage to becoming a substantial obstacle to the growth of Software as a Service (SaaS) models. This study introduces a comprehensive framework designed to secure SaaS supply chains by reducing the risks associated with third-party components. The framework employs C*algebra to represent risk propagation, leverages multi-objective vendor optimization for risk evaluation, and uses machine learning-based dynamic risk assessment to enable continuous, real-time monitoring. It prioritizes risk reduction, improved vendor performance, and compliance with security standards. Validation through simulation showed a total system risk score of 15.7 out of 100, a vendor selection risk score of 8.5/10, and a 92% detection rate for real-time monitoring. The monitoring system was active 99.97% of the time and successfully thwarted 17 out of 20 attacks. The findings indicate that this framework offers an innovative, effective, and adaptive approach to supporting SaaS platforms, delivering markedly improved outcomes in risk propagation control, vendor optimization, and real-time threat assessment.