Implementation Steps:

1. Data Collection:

   • Aggregate vulnerability data from various sources, including internal scans, threat intelligence feeds, and external advisories.
   • Integrate asset data to understand the context and importance of each system within the organization.

2. Risk Scoring:

   • Assign risk scores to each vulnerability based on factors such as exploitability, potential impact, asset criticality, and current threat landscape.
   • Use machine learning algorithms to continuously refine risk scoring models based on historical data and emerging threats.

3. Prioritization:

   • Categorize vulnerabilities into high, medium, and low risk, focusing remediation efforts on high-risk vulnerabilities first.
   • Develop risk-based SLAs (Service Level Agreements) for addressing vulnerabilities, ensuring critical issues are resolved swiftly.

4. Remediation Planning:

   • Create detailed remediation plans for high-risk vulnerabilities, including patch deployment, configuration changes, or other mitigations.
   • Schedule regular reviews to ensure that remediation efforts align with changing risk levels and business priorities.

5. Continuous Monitoring and Reporting:

   • Implement continuous monitoring to detect new vulnerabilities and changes in the risk landscape.
   • Provide real-time dashboards and reports to stakeholders, highlighting the current risk status and remediation progress.