Data Privacy and Security in AI Applications

The study explores the intertwined concepts of data privacy and security in AI, emphasizing clear definitions and accessible explanations to ensure improved clarity and comprehension for a wider audience, emphasizing their importance for ethical and trustworthy AI applications. It defines privacy as control over personal information and security as technical safeguards, highlighting frameworks like India’s Personal Data Protection Bill, 2019, GDPR, and NIST guidelines. Key principles—confidentiality, integrity, and availability—are discussed alongside challenges like data breaches, bias in AI models, and adversarial attacks. The section transitions have been refined for improved structural coherence. Ethical concerns include fairness and transparency in AI systems. Solutions such as encryption, fairnessaware algorithms, and differential privacy are proposed to address these issues while fostering trust in AI technologies.