In penetration testing, efficiently identifying and prioritizing vulnerabilities is crucial for effective risk management. Traditional methods often rely on manual assessments or static scoring systems, which can be time-consuming and inconsistent. This research project implements an AI-driven framework that automates vulnerability prioritization and remediation recommendations, enhancing penetration testing efficiency. The system leverages data from the National Vulnerability Database (NVD), processing vulnerabilities based on their Common Vulnerability (CVE) scores and descriptions. It scans a target system and presents the user with a ranked list of the most critical vulnerabilities for immediate attention. Additionally, the tool integrates AI to provide detailed remediation guidance for each identified issue, offering actionable, context-specific advice on mitigation. This powerful dual approach accelerates decision-making, empowering security teams to swiftly neutralize the most critical threats with precision while leveraging expert-driven intelligence for maximum defense effectiveness.
