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.
Keywords: Adversarial attacks, Algorithmic transparency, Artificial intelligence (AI), Availability, Bias in AI, Consent, Confidentiality, Cybersecurity, Data breaches, Data privacy, Data security, Differential privacy, Encryption standards, Ethical AI, Fairness-aware machine learning, Federated learning, General data protection regulation (GDPR), Personal data protection bill, 2019.
