Team
Dev Limbachia, Yuvraj Patel, Murtuza Mohammed, Anjali Patel, Harsh Shah
Categories
Artificial Intelligence
Overview
The cybersecurity risk assessment prediction initiative, backed by Technology Helps, has introduced a cutting-edge AI-powered web application. This solution seamlessly integrates staff training evaluations, phishing risk analyses, vulnerability assessments, and security controls and audit scores to generate weighted scores and automate risk categorizations.
Employing a Random Forest Classifier, the model achieves an impressive 95% accuracy rate and a weighted average F1 score of 0.91. Leveraging OutSystems, we've developed a user-friendly web application, which is further enhanced through integration with AWS for model training and development via Lambda and S3. This setup ensures dynamic risk score categorization based on industry standards, empowering enterprises with real-time insights for proactive cybersecurity risk management.
Capstone Project - The Decoders (CyberSecurity Risk Assessment Score Prediction)
The cybersecurity risk assessment prediction initiative, backed by Technology Helps, has introduced a cutting-edge AI-powered web application. This solution seamlessly integrates staff training evaluations, phishing risk analyses, vulnerability assessments, and security controls and audit scores to generate weighted scores and automate risk categorizations. Employing a Random Forest Classifier, the model achieves an impressive 95% accuracy rate and a weighted average F1 score of 0.91. Leveraging OutSystems, we've developed a user-friendly web application, which is further enhanced through integration with AWS for model training and development via Lambda and S3. This setup ensures dynamic risk score categorization based on industry standards, empowering enterprises with real-time insights for proactive cybersecurity risk management.
