HELIDORICE MACKSENZIUS MWALONGO
AI & Data Science UDOMREDTRACE
Problem Statement
Problem Statement Organizations in Tanzania, including government institutions, universities, SMEs, and enterprises, increasingly rely on digital systems but often lack effective ways to continuously determine whether their cybersecurity controls can detect suspicious activities. Security teams receive large volumes of network, endpoint, authentication, and system logs, making manual identification of abnormal behavior slow and difficult. Traditional security assessments are also often periodic, leaving detection gaps between assessments. REDTRACE TZ addresses this challenge by using structured machine-learning techniques, primarily anomaly detection and classification, to analyze security telemetry generated during authorized security exercises, identify unusual behavior, classify potential threat activities, and highlight weaknesses in an organization's detection capability.
Proposed Solution
Proposed Solution REDTRACE TZ is an AI-powered cybersecurity validation platform that uses machine learning, primarily anomaly detection and classification, to identify suspicious behavior within authorized networks and cyber-range environments. The system will collect structured security data such as network traffic, authentication events, endpoint activities, DNS requests, and security logs, then establish normal behavior patterns and automatically detect deviations. Detected anomalies will be classified into threat-behavior categories and assigned risk scores based on severity and asset importance. The platform will provide a dashboard showing detected activities, detection performance, missed threats, and security gaps, enabling SOC analysts, IT teams, and security managers to evaluate and strengthen their cybersecurity controls.
Innovation
InnovationREDTRACE TZ combines AI-driven anomaly detection, threat classification, and controlled adversary-emulation data to create an intelligent cyber-resilience validation platform. Unlike conventional vulnerability scanners that mainly identify weaknesses, REDTRACE TZ learns normal system behavior, detects deviations, classifies suspicious activities, and automatically generates a Cyber Resilience Score showing how effectively an organization detects simulated threats. This transforms cybersecurity assessment from periodic manual testing into a continuous, measurable, and data-driven process, while AI automates telemetry analysis, prioritization, and security-gap identification.
Expected Impact
Expected ImpactREDTRACE TZ will improve cybersecurity readiness by enabling organizations to detect suspicious activities earlier, identify gaps in their security controls, and prioritize high-risk weaknesses using data-driven insights. It will reduce the time and effort required to analyze large volumes of security data through AI automation, while providing measurable Cyber Resilience Scores to track improvement over time. The platform will also support practical Red Team and Blue Team training in universities and cybersecurity institutions, helping develop Tanzania’s local cybersecurity workforce and strengthening the overall resilience of organizations against evolving cyber threats
Standing
Team Members
Mentor
NOEL MOSES MWADENDE