Data Privacy and Security in AI

Kakarala, Manikanta kumar and Rongali, Sateesh Kumar (2025) Data Privacy and Security in AI. World Journal of Advanced Research and Reviews, 25 (3). pp. 555-561. ISSN 2581-9615

The advancement of artificial intelligence (AI) technology has created both opportunities and risks in data protection and safeguarding. Given that numerous organizations are now employing AI systems in various industries, the goal of using data to drive innovation has never been urgent. This paper will review the relationship between AI, data privacy, and security and discuss the current issues and possible recommendations. Furthermore, this study introduces new approaches, including federated learning and homomorphic encryption, which preserve data integrity while still using the data. Using concrete examples from various industries, the study reveals practices and tendencies that company leaders should follow and avoid to achieve a proper balance. This paper offers an ethical approach that integrates practical recommendations for policymakers, technologists, and businesses to build user trust and progress responsibly and technically. Since most AI applications are based on big data, users’ data protection and systems’ performance and expandability are extremely important. This paper explores the issue of data privacy and security in AI and discusses promising strategies to address the problem, and guidelines for responsible AI implementation. The main priorities include the exposition of the algorithms, data anonymization methods, legal requirements, and strengthening cybersecurity. This paper presents real-life examples, and industry benchmarks to support the framework that can help organizations manage technologies in a way that addresses ethical concerns. In the future, the analysis presented in the study can help industries understand trends that help develop AI strategies that meet high privacy and security standards.