@article{ART003314936},
author={Myoungseob Mun},
title={Artificial Intelligence Training Data and Data Security},
journal={Legal Theory & Practice Review},
issn={2288-1840},
year={2026},
volume={14},
number={1},
pages={13-46}
TY - JOUR
AU - Myoungseob Mun
TI - Artificial Intelligence Training Data and Data Security
JO - Legal Theory & Practice Review
PY - 2026
VL - 14
IS - 1
PB - The Korea Society for Legal Theory and Practice Inc.
SP - 13
EP - 46
SN - 2288-1840
AB - Concerns over data security, initially triggered by 5G supply-chain security anxieties, are now evolving into an “arms race for training data” in the era of AI technological hegemony, emerging as a core issue surrounding national strategic resources. Doubts raised by the Huawei-related controversy—particularly regarding potential technical vulnerabilities of Chinese firms and the possibility of state control—expanded into fears of confidential data exfiltration, thereby strengthening the perceived need for national intervention in data security. This security-driven context has contributed to a growing tendency among major jurisdictions, including the United States, the European Union (EU), and China, to reinforce data sovereignty and tighten restrictions on cross-border data transfers on the grounds of national security, personal data protection, and industrial policy. In particular, the rapid advancement of generative AI has made model performance and international competitiveness increasingly dependent on access to vast quantities of high-quality training data. However, the accelerating trend toward limiting overseas data transfers and mandating domestic storage and processing is creating substantial barriers to securing training data, effectively transforming AI competition into competition over data acquisition. Consequently, data security has become a critical factor that extends beyond the protection of information to directly shape AI model capability and national competitiveness. Against this backdrop of competing imperatives, this study explores policy and institutional options that can simultaneously promote AI innovation while safeguarding data sovereignty and national security. To this end, it examines the data security legal and regulatory frameworks of the United States, the EU, and China through the dual lenses of “free flow of data” and “security-oriented control.”
KW - Data Security;Data Localization;Training Data;Fair Use;Data Act
DO -
UR -
ER -
Myoungseob Mun. (2026). Artificial Intelligence Training Data and Data Security. Legal Theory & Practice Review, 14(1), 13-46.
Myoungseob Mun. 2026, "Artificial Intelligence Training Data and Data Security", Legal Theory & Practice Review, vol.14, no.1 pp.13-46.
Myoungseob Mun "Artificial Intelligence Training Data and Data Security" Legal Theory & Practice Review 14.1 pp.13-46 (2026) : 13.
Myoungseob Mun. Artificial Intelligence Training Data and Data Security. 2026; 14(1), 13-46.
Myoungseob Mun. "Artificial Intelligence Training Data and Data Security" Legal Theory & Practice Review 14, no.1 (2026) : 13-46.
Myoungseob Mun. Artificial Intelligence Training Data and Data Security. Legal Theory & Practice Review, 14(1), 13-46.
Myoungseob Mun. Artificial Intelligence Training Data and Data Security. Legal Theory & Practice Review. 2026; 14(1) 13-46.
Myoungseob Mun. Artificial Intelligence Training Data and Data Security. 2026; 14(1), 13-46.
Myoungseob Mun. "Artificial Intelligence Training Data and Data Security" Legal Theory & Practice Review 14, no.1 (2026) : 13-46.
