Vulnerability Prediction

What Is Vulnerability Prediction

Vulnerability Prediction uses data analysis and machine learning to forecast the likelihood that a specific software vulnerability will be exploited. It often considers factors like vulnerability characteristics, threat intelligence, and historical exploit data.

Why Is Vulnerability Prediction Important

Predicting exploit likelihood helps organizations prioritize vulnerability remediation efforts. It allows teams to focus resources on fixing the vulnerabilities that pose the most immediate risk, rather than just relying on severity scores alone.

Example Of Vulnerability Prediction

Using the Exploit Prediction Scoring System (EPSS), a team identifies a vulnerability with a high CVSS score but a low EPSS score (low predicted likelihood of exploitation). They decide to prioritize patching a different vulnerability with a moderate CVSS score but a very high EPSS score.

How To Implement Vulnerability Prediction

• Gather data on past vulnerabilities and exploits
• Integrate threat intelligence feeds
• Use predictive models like EPSS or develop custom algorithms
• Set thresholds for prioritization based on risk appetite
• Integrate prediction scores into the vulnerability management workflow

Best Practices

• Combine prediction scores with asset criticality for context
• Regularly update models with new threat data
• Focus remediation efforts based on predicted risk, not just severity