## 4 Results and Discussion
### Discussion and Interpretation  
The analysis revealed that DeepL and ChatGPT were the most consistent in terms of fidelity to the reference translation provided by INCIBE. DeepL, in particular, emerged as the most effective in preserving INCIBE's style, achieving the highest number of \"wins\" compared to the other translators, indicating a more robust overall performance. In contrast, ChatGPT represents an efficient, cost-free alternative, demonstrating competitive consistency. Although Copilot performed well in certain tests, it displayed inconsistencies, suggesting the need for adjustments to improve its reliability. Bing Translator yielded the lowest results in terms of adherence to the reference translation. Moreover, variability among translators, particularly in how they handle context, significantly influences the results. This variability could explain why Copilot, while more efficient for free-form translation tasks, may require specific adjustments or training to align more closely with predefined reference translations. In conclusion, machine translators outperform generative AI systems across all evaluation metrics when the objective is to produce a translation that closely matches the INCIBE reference. While GenAI tools excel at generating more natural and contextually adaptive translations, their inherent flexibility limits their effectiveness in this particular task. Furthermore, as anticipated, the quality of the corpus significantly impacts the evaluation of translations using word embeddings. Across all models, GenAI exhibits greater variability, suggesting that while it can generate translations, it also produces less consistent results. Machine translators, on the other hand, occupy a middle ground, demonstrating more homogeneous performance without achieving significant superiority in any particular model. As limitations, the extension of the dataset may affect the results. The performance of the machine translation systems either degrades or improves depending on the scenario. It would be necessary to expand the dataset without computational or time constraints, in order to draw more generalizable conclusions. However, we believe that the results presented in this study can serve as a solid starting point for designing a more comprehensive experiment in terms of resources, translation systems, and types of descriptions analyzed, taking into account the findings reported here. Moreover, although human judgment is undoubtedly crucial for evaluating translation quality, the very absence of a human agent is precisely what this work seeks to assess. In this context, relying on a manual process not only for generating translations but also for reviewing them would be counterproductive in terms of time and cost, especially given the large and continuous volume of translations required. In any case, it is important to note that the dataset includes the version translated by INCIBE, which has already been revised by professional human translators. By measuring how closely the generated translations align with INCIBE's official version, we aim to incorporate the human factor into the evaluation process. Another potential limitation of the study is explained as follows: Automation in this domain is feasible because the early vulnerability alert service simply provides a Spanish replica translation of the vulnerabilities published by NIST. On the other hand, it is not an incident response service; it is a translation service. The high sensitivity of the alerts is defined by other standards, which follow their own procedures and standardized taxonomies. As previously explained, the goal is not to fully automate the entire process but rather to streamline the translation phase while ensuring a minimum guaranteed level of quality. For this reason, it would indeed be necessary to periodically assess the quality of the translations to ensure they remain at an adequate level. However, it is not necessary to evaluate translation performance in real time.