Global losses due to cybercrime are expected to top 10 trillion dollars by 2025, in which Small and Medium-sized Business (SMBs) will be disproportionately affected due to not using enterprise-grade defenses. Traditional vulnerability scanners emphasize detection yet generate dense technical reports that overwhelm non-specialist administrators. This paper describes a generative AI–powered platform for automated scanning using Nmap (Network Mapper), OpenVAS (Open Vulnerability Assessment Scanner), OWASP ZAP (Open Web Application Security Project Zed Attack Proxy), and Nessus; explainable tutoring by Google Gemini; and sandboxed remediation using Anthropic Claude in Docker containers to provide isolation along with rollback safety. A design-science methodology is followed for evaluation on detection accuracy, explanation clarity, remediation reliability, and usability. The results indicate that over 80% of remediation succeeded, 35% improved comprehension of the users, and over 85% completed their tasks, which confirms the hypothesis that generative tutoring reduces cognitive load while containerization allows for safe and reproducible remediation. This kind of framework underpins generative AI as an accessible democratizing tool in conducting cybersecurity operations and education.