An ML-based scoring engine uses machine learning algorithms to evaluate and prioritize security threats based on risk, behavior patterns, and historical data. By analyzing large volumes of alerts, logs, and threat intelligence, it assigns dynamic risk scores to vulnerabilities, anomalies, or indicators of compromise (IOCs), helping security teams focus on the most critical threats first. Unlike static rule-based systems, this engine continuously learns and adapts to evolving attack techniques, reducing false positives and improving detection accuracy. Integrating an ML-based scoring engine into your security infrastructure enhances decision-making, streamlines incident response, and supports a more proactive cybersecurity posture.