Proactive AI-Driven Cybersecurity for Endangered Language Preservation: Safeguarding the Suba Linguistic Corpus
Hesborn Ondiba · 2025
This paper explores the integration of proactive AIdriven cybersecurity measures in developing endangered language corpora, with a case study focusing on the Suba language of Kenya. Given the sensitive nature of linguistic data and the cultural significance embedded within, securing the Suba language corpus against cyber threats is critical to its preservation. This study combines a comprehensive literature review on endangered language preservation, AI applications, and cybersecurity risks with the author’s practical experience in corpus development. By leveraging machine learning, anomaly detection, and blockchain-based access control, this paper proposes a robust framework for securing linguistic data from unauthorized access, data corruption, and breaches during collection, transmission, and storage. It emphasizes ethical data management and the role of AI technologies in safeguarding the integrity and confidentiality of Indigenous knowledge, contributing to broader efforts in preserving linguistic diversity.