Generative Artificial Intelligence and Machine Translators in Spanish Translation of Early Vulnerability Cybersecurity Alerts

Abstract

The increasing reliance on artificial intelligence in cybersecurity has broadened the role of generative artificial intelligence in tasks such as text generation and translation. This study assesses the effectiveness of generative artificial intelligence and conventional translation tools in translating early vulnerability alerts from English to Spanish—a critical process for ensuring the timely dissemination of cybersecurity information. Utilizing a dataset provided by the Spanish National Cybersecurity Institute, translations were generated using various systems and evaluated through linguistic assessment metrics, including methods measuring lexical similarity and others capturing semantic meaning beyond direct word matching. Additionally, word embeddings were employed to enhance the accuracy of semantic similarity analysis. The results indicate that conventional translation tools generally exhibit greater accuracy and structural fidelity, whereas generative artificial intelligence produces more natural-sounding translations. However, this flexibility results in greater variability in translation quality. The findings suggest that while generative artificial intelligence may serve as a valuable complement to traditional tools, its inconsistencies may limit its suitability for highly technical content that demands precision. This study underscores the importance of integrating both approaches to improve the accuracy and accessibility of cybersecurity alerts across different languages.