ALERT

Small and Medium Enterprises (SMEs) often face challenges in managing the overwhelming number of security alerts generated by their IT systems. Traditional alert systems lack contextual intelligence, leading to alert fatigue, delayed responses, and missed critical incidents. This study presents a context-aware Alert Management System that enhances prioritization accuracy by incorporating operational factors such as alert frequency, entity type, business hours, and historical severity. The system was designed and implemented using a React-based simulation environment with 50 synthetic alerts representing realistic SME security events. Comparative evaluation between a baseline model ((Severity + Criticality)/2) and an enhanced model ((Severity + Criticality + Context Factor)/3) demonstrated a 42.42% reduction in alert fatigue and
complete elimination of false-positive high-priority alerts while maintaining 100% detection of genuine threats. The findings confirm that context-aware alert management significantly improves prioritization accuracy and analyst efficiency. The proposed framework provides SMEs with a cost-effective, transparent, and scalable solution for strengthening their cybersecurity posture and improving real-time incident response.