This paper introduces an advanced application of Large Language Models (LLMs) to predict Means of Compliance (MoCs) in aerospace defense systems, solely based on textual descriptions of system requirements. Amid increasing complexity and escalating demands on compliance verification processes, this study leverages a meticulously curated dataset of labeled requirements to train a fine-tuned model that automates MoC assignments. By incorporating machine learning classification techniques, the model demonstrates significant potential to enhance the efficiency and reliability of system verifications, markedly reducing the time and human effort traditionally required. A comparative analysis, incorporating feedback from Brazilian Aerospace Defense specialists, underscores the model's capability to match, and at times surpass, human accuracy in MoC identification, thereby supporting the development of more robust defense systems. This work not only contributes to the ongoing discourse on the integration of AI in systems development but also proposes a scalable solution to streamline compliance processes in the aerospace industry.