Traditional vulnerability scanners like Nmap and Nessus provide powerful detection, but they often miss context-specific risks. A new proposal suggests layering machine learning on top of existing scan results to prioritize remediation based on environmental baselines rather than just known CVEs. This hybrid approach aims to reduce alert fatigue by focusing on what is abnormal for a specific network.

Rule-based systems excel at flagging deprecated protocols or exposed admin services. However, they struggle with gradual security drift or accidental misconfigurations. By training models on historical scan data, defenders can detect deviations from established patterns. This method learns what constitutes normal exposure for an environment, flagging unusual service combinations or sudden port changes that traditional tools might ignore.

The proposed architecture keeps scanning deterministic while using ML for interpretation. It does not replace scanners but adds a layer of anomaly detection to highlight high-risk deviations. This approach requires significant historical data to be effective and remains exploratory. The goal is to bridge the gap between raw technical data and actionable, context-aware defensive decisions.