Generative Artificial Intelligence and Machine Translators in Spanish Translation of Early Vulnerability Cybersecurity Alerts  
[PERSON]  
1 Research Institute of Applied Sciences in Cybersecurity (RIASC), Universidad de Leon, Campus de Vegazana, s/n, 24007 Leon, Spain; [EMAIL_ADDRESS] (J.R.M. ); [EMAIL_ADDRESS] (D.T.R. ); [EMAIL_ADDRESS] (M.E.O.C. ); [EMAIL_ADDRESS] (I.S.E.) [PERSON]  
1 Research Institute of Applied Sciences in Cybersecurity (RIASC), Universidad de Leon, Campus de Vegazana, s/n, 24007 Leon, Spain; [EMAIL_ADDRESS] (J.R.M. ); [EMAIL_ADDRESS] (D.T.R. ); [EMAIL_ADDRESS] (M.E.O.C. ); [EMAIL_ADDRESS] (I.S.E.) [PERSON]  
1 Research Institute of Applied Sciences in Cybersecurity (RIASC), Universidad de Leon, Campus de Vegazana, s/n, 24007 Leon, Spain; [EMAIL_ADDRESS] (J.R.M. ); [EMAIL_ADDRESS] (D.T.R. ); [EMAIL_ADDRESS] (M.E.O.C. ); [EMAIL_ADDRESS] (I.S.E.) [PERSON]  
1 Research Institute of Applied Sciences in Cybersecurity (RIASC), Universidad de Leon, Campus de Vegazana, s/n, 24007 Leon, Spain; [EMAIL_ADDRESS] (J.R.M. ); [EMAIL_ADDRESS] (D.T.R. ); [EMAIL_ADDRESS] (M.E.O.C. ); [EMAIL_ADDRESS] (I.S.E.) [PERSON]  
1 Research Institute of Applied Sciences in Cybersecurity (RIASC), Universidad de Leon, Campus de Vegazana, s/n, 24007 Leon, Spain; [EMAIL_ADDRESS] (J.R.M. ); [EMAIL_ADDRESS] (D.T.R. ); [EMAIL_ADDRESS] (M.E.O.C. ); [EMAIL_ADDRESS] (I.S.E.) Footnote 1: email: {j.j.  
the creation of sophisticated malware, the automated dissemination of disinformation, and identity fraud. Moreover, new attack vectors have emerged that specifically target vulnerabilities within GenAI systems to gain unauthorized access to sensitive information. This article presents a use case for the application of generative AI in a previously unexplored area of cybersecurity: the translation of early vulnerability alerts into Spanish. According to the National Institute of Standards and Technology (NIST), a vulnerability is defined as a \"weakness in a system that could be exploited by a threat\" [4]. Vulnerabilities can manifest in hardware, software, or processes and may result from programming errors, misconfigurations, or design flaws. Identifying existing vulnerabilities, applying timely updates, and deploying patches are critical measures to minimize the risk of exploitation. Early warning mechanisms play a crucial role in vulnerability management, allowing organizations to identify and mitigate risks before they can be exploited. However, this proactive approach may be less effective if vulnerability-related information is not accessible in the primary language of a significant portion of the cybersecurity community. In this context, the Spanish National Cybersecurity Institute (INCIBE) provides a publicly accessible database containing Spanish-language replicas of the latest documented vulnerabilities originally published in English by NIST (see [5]). This repository, comprising over 200,000 records, is maintained through manual translations performed by professional translation teams under a collaboration agreement between INCIBE and NIST. Each vulnerability entry includes technical descriptions, severity assessments, publication and update dates, impact evaluations, and available mitigation strategies. While this resource is invaluable to researchers and cybersecurity professionals, the manual translation process is time-intensive. The manual translation of vulnerability descriptions entails a substantial daily workload. Given that these descriptions must be processed rapidly and efficiently to serve as effective early warnings, any delay in their translation and dissemination could compromise the security of affected systems. Additionally, the dynamic nature of vulnerabilities necessitates continuous updates, further complicating the translation process. These factors underscore the need for automation in this domain. With this objective in mind, this study aims to identify the most accurate machine translation system for this task.