AI-driven advanced python pipeline for windows operating systems vulnerability detection and exploitation

The complexity of cyber threats on Windows operating systems grows exponentially, conventional vulnerability management solutions have failed to keep up with the pace.

The aim of this paper is to describe the creation and execution of an Artificial Intelligence-driven, automated vulnerability discovery and exploitation pipeline. Built using powerful technological tools like Nmap, Niko, and Metasploit, combined with AI model from OpenAI frameworks and GROQ, the program is using automation of the vulnerability management lifecycle starting from detection (reconnaissance) to patch recommendations. We use the program to build modular architecture with Python-based orchestration, with an interactive website (front-end) interface, and an AI analysis engine that contextualizes hazards and generates attack code dynamically for the back-end. We establish this using a controlled virtualized environment to minimize risk exposure, where the system successfully detects and exploits critical vulnerabilities like Print Nightmare (CVE-2021-34527) and Eternal Blue (CVE-2017-0144).

Using an AI-powered patch suggestions has proven to be very relevant, with an average score of 4.9/5. This strategy shows significant Improvements with detection and exploitation accuracy, while reducing patch deployment time by 35% and human labor by 20%. These findings have highlighted the revolutionary potential of an AI-enhanced vulnerability management that can be adopted for contemporary cybersecurity concerns, while providing its scalability and resiliency approach for safeguarding Windows-centric settings.