CVE Triage Automation: Building Machine Learning Models to Prioritize Critical Zero-Day Patches Based on Real-World Exploit Pat…

Security teams are overwhelmed with 2,000-3,000 monthly vulnerability alerts, leading to critical vulnerabilities being missed. Machine learning models can help prioritize patches based on real-world exploit patterns. These models should be trained on actual exploitation data, not theoretical CVSS scores. Successful models incorporate technical features like CVSS metrics and threat intelligence indicators like exploit complexity and asset criticality. Engineers should focus on building effective ML models that capture both technical and threat intelligence aspects.