Automated Risk Prioritization and Mitigation System (ARPMS)

Managing risks in Agile IT projects particularly for small and medium enterprises (SMEs), is a complex challenge due to the iterative and dynamic nature of Agile methodologies. Conventional risk management frameworks frequently prove inadequate for these complexities, resulting in possible delays, budget excesses, and diminished quality. In this paper, we present a Risk Prioritization and Mitigation System as a solution that utilizes Artificial Intelligence to automate the risk detection, prioritization, and mitigation processes in Agile workflows. Employing machine-learning algorithms like XGBoost classifiers and data preprocessing methods like Principal Component Analysis (PCA) for dimensionality reduction, ARPMS classifies the data into the required risk levels (low, medium, and high risk) with almost 100% accuracy. The system aims to interface with Agile tools, providing real-time risk alerts and actionable insights via an interactive dashboard. Testing performed on an incident log and credit risk data sets proved ARPMS's capability to mitigate risk impact, enhance decision-making, and optimize project results. Future improvements include scaling the datasets, adding real-time data streams, and further investigation for adaptive prioritization. ARPMS integrates predictive analytics with Agile principles, providing a scalable and robust risk management approach that enables teams to proactively enhance project resilience.