Manish, Sanwal (2025) The Dangers of Deploying DeepSeek R1 in Enterprise Environments: Post-2025 LLM Analysis. Information Horizons: American Journal of Library and Information Science Innovation, 3 (5). pp. 10-13. ISSN 2993-2777

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Abstract

DeepSeek R1, introduced in early 2025, has garnered attention for its cutting-edge language and predictive capabilities. However, emerging community reports and analyses highlight significant risks tied to security, data handling, and compliance—particularly for enterprises leveraging large language models (LLMs) at scale. This paper expands on previous findings to integrate newly available research on LLM security. We examine how DeepSeek R1’s training data discrepancies, potential cross-border data transfers, and inherent vulnerabilities align with broader enterprise concerns about generative AI. We conclude with actionable recommendations for organizations seeking to responsibly adopt DeepSeek R1 while minimizing security and compliance pitfalls.

Item Type:	Article
Subjects:	L Education > L Education (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	10 Jun 2025 05:38
Last Modified:	10 Jun 2025 05:38
URI:	http://eprints.umsida.ac.id/id/eprint/16195

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