THE IMPACT OF THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE SYSTEMS ON THE STORAGE OF CONFIDENTIAL INFORMATION IN THE REPUBLIC OF UZBEKISTAN
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Abstract:
The article analyzes the impact of artificial intelligence (AI) technology development on the practice of storing and protecting personal data of citizens in the context of Uzbekistan's legal framework. The author examines the Law "On Personal Data" as the basic document regulating the collection, storage, and use of confidential information. It is noted that the implementation of AI systems opens new opportunities for improving data protection mechanisms, but at the same time carries potential risks of leaks and misuse. The article provides recommendations for adapting the regulatory framework to new realities, including introducing requirements for transparency and accountability of AI algorithms, strengthening the rights of data subjects, and implementing risk assessment mechanisms. The article emphasizes the need for a comprehensive approach involving government, business, and society to ensure a balance between technological development and privacy protection.
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О персональных данных: Закон Республики Узбекистан от 2 июля 2019 года № ЗРУ-547;
Постановление Президента Республики Узбекистан Об утверждении Стратегии развития технологий искусственного интеллекта до 2030 года от 14.10.2024 г. № ПП-358
Microsoft Corporation. (2021). Microsoft Information Protection: Technical documentation. Microsoft Docs. https://docs.microsoft.com/en-us/information-protection/
OneTrust LLC. (2021). Privacy Management Software Platform. OneTrust Technical Documentation. https://www.onetrust.com/products/privacy-management/
Organisation for Economic Co-operation and Development. (2021). Recommendation of the Council on Artificial Intelligence. OECD Legal Instruments.
Recursion Pharmaceuticals. (2021). Application of generative adversarial networks for medical imaging synthesis. Recursion Research Publications.
Smith, J., & Johnson, B. (2020). Privacy vulnerabilities in large language models: A case study of GPT-2. In Proceedings of the International Conference on Machine Learning and Cybersecurity (pp. 123-145). IEEE.