<![CDATA[This research focuses on the analysis of data security risks in Artificial Intelligence (AI) systems, particularly in the context of the growing challenges posed by the digital era. With the increasing reliance on AI for processing sensitive data, vulnerabilities such as adversarial attacks, privacy violations, and data breaches have become significant concerns. The primary objective of this study is to identify these risks, evaluate existing protection strategies, and propose effective solutions to enhance data security in AI systems. A mixed-methods approach was employed, combining a comprehensive literature review with qualitative and quantitative data collection, including case studies, expert interviews, and statistical analysis of AI security incidents. The results revealed that while traditional security measures like encryption and access control are essential, they are insufficient to address the unique risks posed by AI technologies. Emerging techniques such as federated learning, differential privacy, and adversarial training were found to offer promising solutions but face challenges in terms of implementation and model accuracy. The research concluded that a holistic approach, integrating both traditional cybersecurity practices and AI-specific strategies, is necessary to safeguard sensitive data in AI systems. This study contributes to the field by offering practical insights into current AI security issues and proposing recommendations for improving data protection mechanisms. Future research should focus on enhancing the scalability and efficiency of these protection strategies to ensure their effective application in diverse real-world AI systems.]]>
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