In a corporate environment where industrial secrets and critical assets need protection, ensuring robust security is essential. Misconfigurations, improper setups in systems or applications, can create exploitable vulnerabilities. These issues often derive from overlooked security best practices, such as unchanged default settings, excessive permissions, or wrong protocol configurations. This master’s thesis explores an AI-driven solution that leverages Natural Language Processing (NLP) techniques and Large Language Models (LLMs) to mitigate security risks. The aim is to develop a tool that not only assists users in analyzing network configurations and resolving issues, but also focuses on anticipating and preventing attacks by proactively managing security risks.