Generation and Validation of Configuration Management Code for Cyber Range Environments Using Large Language Models

Abstract

This research explores the use of large language models (LLMs) to automate cyber range sandbox configuration with SaltStack. LLMs translate natural language prompts into executable SaltStack states, streamlining the environment setup, and reducing manual scripting. An LLM-controlled proxy manages a SaltStack master, enabling on-demand configuration for various use cases. A second LLM validates the generated configurations to ensure correctness. This approach improves flexibility, adapts to changing requirements, and demonstrates the potential of natural language-driven configuration management for secure testing and development environments.