Securing Meaning: Language Equity in Cybersecurity Translation

Abstract

As cybersecurity becomes a cornerstone of global higher education, language has emerged as an unexpected point of vulnerability. Machine translation (MT) tools, increasingly used to render cybersecurity policies into multiple languages, often distort meaning by translating technical terms literally rather than conceptually. Words like firewall, phishing, or backdoor lose their intent in translation, creating barriers to comprehension and leaving multilingual learners at risk of misunderstanding critical policies. This paper explores the intersection of cybersecurity vocabulary, machine translation, and language equity, drawing on examples of mistranslations and language acquisition research to demonstrate how linguistic gaps can weaken both institutional safeguards and student confidence.

We argue that cybersecurity education must treat terminology with the same precision as code, recognizing that mistranslation not only undermines clarity but also compounds anxiety for multilingual learners navigating complex technical content. To address these challenges, the paper examines strategies such as the use of back-translation, custom glossaries, Universal Design for Learning (UDL) frameworks, and emerging AI translation tools like custom ChatGPT models. Together, these approaches highlight a pathway for higher education to balance inclusion with accuracy, ensuring that policies and coursework maintain both technical rigor and accessibility. By reframing cybersecurity not only as a technical field but also as a linguistic one, this research calls for a more intentional, equity-driven approach to translation that secures both data and learning outcomes.