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Risk-based vulnerability management software helps organizations identify and prioritize vulnerabilities by assessing them against customizable risk factors. Unlike traditional vulnerability management tools, these advanced solutions leverage machine learning algorithms to prioritize vulnerabilities and guide the execution of remediation efforts based on data-driven insights. Companies use these risk-based solutions to evaluate their entire IT infrastructure, including on-premises systems, cloud services, and applications, to establish priority areas. Instead of addressing vulnerabilities in the order they are discovered, organizations can automate the process to focus first on vulnerabilities that affect critical business functions. They can then tackle issues based on their severity and the time required for remediation, with the flexibility to adjust priorities according to specific risk factors. Primarily used by IT and security teams, risk-based vulnerability management solutions allow professionals to integrate system and application data, set priorities, and analyze assets more efficiently. Automation significantly reduces manual effort, and addressing the most critical vulnerabilities first minimizes the risk of security breaches, system failures, and data loss. While there is some overlap with security risk analysis tools, risk-based vulnerability management software stands out by incorporating machine learning and automation. Security risk analysis tools share similar functionality in identifying vulnerabilities but generally lack the advanced prioritization and remediation support offered by risk-based solutions.