  The widespread adoption of microservice architectures has given rise to a new
set of software security challenges. These challenges stem from the unique
features inherent in microservices. It is important to systematically assess
and address software security challenges such as software security risk
assessment. However, existing approaches prove inefficient in accurately
evaluating the security risks associated with microservice architectures. To
address this issue, we propose CyberWise Predictor, a framework designed for
predicting and assessing security risks associated with microservice
architectures. Our framework employs deep learning-based natural language
processing models to analyze vulnerability descriptions for predicting
vulnerability metrics to assess security risks. Our experimental evaluation
shows the effectiveness of CyberWise Predictor, achieving an average accuracy
of 92% in automatically predicting vulnerability metrics for new
vulnerabilities. Our framework and findings serve as a guide for software
developers to identify and mitigate security risks in microservice
architectures.
