## 5 Conclusions  
The early warning of vulnerabilities plays a critical role in protecting businesses, institutions, and organizations by enabling proactive risk mitigation and response strategies. The precise translation of these alerts into Spanish enhances accessibility, ensuring that key stakeholders can swiftly comprehend and respond to critical security threats. This process not only strengthens cybersecurity awareness but also facilitates the effective dissemination of knowledge within the broader community, fostering a more resilient and informed ecosystem against emerging threats. The integration of machine translation tools can further enhance the efficiency of automatically translating vulnerability alerts. The paper presents a contribution to the field of cybersecurity and linguistics by providing a comprehensive evaluation of GenAI and machine translation tools in the context of translating cybersecurity vulnerability alerts. This study evaluates the performance of various machine translation systems, including generative AI tools, in translating vulnerability descriptions. The results show that, overall, dedicated translation tools such as DeepL and Google Translate tend to offer slightly higher structural accuracy and style consistency. However, generative AI tools like ChatGPT and Copilot demonstrate remarkable adaptability and produce fluent, natural-sounding translations, especially for shorter, less technical descriptions. The methodology adopted in this study--based on the use of diverse evaluation metrics and embedding-based semantic similarity models--has enabled a nuanced and multidimensional analysis of translation quality. It also highlighted significant differences in how tools handle short versus long descriptions, revealing that longer, more complex entries tend to degrade translation performance more noticeably across all systems. The main advantage of using generative AI in this context lies in its flexibility, scalability, and the ability to integrate it into broader AI-powered workflows. Despite not being specifically designed for translation, GenAI tools offer competitive performance while facilitating automation and reducing human workload--particularly useful in time-sensitive cybersecurity environments. Furthermore, their prompt-based nature makes them highly customizable, opening the door to domain-specific fine-tuning. Compared to other tools currently on the market, the proposed approach demonstrates strong feasibility: most of the tested tools are publicly available, require minimal technical setup, and support automation (with limitations). While licensing constraints and lack of API access pose challenges for full automation in real-world secure environments such as INCIBE, the results confirm that a hybrid model--leveraging GenAI for initial translation and traditional tools or human revision for validation--may offer an optimal balance between efficiency and quality. In summary, this work not only provides a practical benchmark for selecting machine translation tools in the cybersecurity field but also contributes to emerging research lines at the intersection of natural language processing, multilingual threat intelligence, and human-AI collaboration. Future research may focus on prompt engineering strategies, fine-tuning GenAI models for security-specific content, or exploring real-time implementation in secure operational settings. Future research should focus on expanding the dataset, optimizing prompts, and enhancing translations through data enrichment. We propose the hypothesis that cybersecurity-trained translation models could achieve greater precision and coherence. Comparing general-purpose tools with specialized databases may significantly improve semantic fidelity, making domain-specific generative AI systems more effective for translating vulnerability alerts. Also, a broader study encompassing various types of cybersecurity texts could indeed offer a more comprehensive view of the role of GenAI in this domain.