Artificial Intelligence and Data Privacy: Protecting Information in the Digital Age

Abstract The rapid proliferation of Artificial Intelligence (AI) across healthcare, finance, e-commerce, and public governance has introduced unprecedented challenges to data privacy. AI systems rely on vast quantities of personal and sensitive data to train, optimize, and deploy predictive models, creating complex tensions between utility and individual privacy rights. This comprehensive review explores the intersection of AI capabilities and data privacy obligations, examining the technical mechanisms through which privacy can be compromised, the regulatory frameworks that govern data handling, and the emerging technologies designed to reconcile AI performance with privacy preservation. The paper surveys Privacy-Enhancing Technologies (PETs) such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation. It further examines the adequacy of global regulatory regimes including GDPR, CCPA, and India's Digital Personal Data Protection Act, and critically evaluates their enforcement in AI-driven environments. The findings highlight persistent trade-offs between model accuracy and privacy guarantees, scalability barriers in cryptographic approaches, and the need for standardized privacy metrics. Future directions include privacy-aware AI architectures, explainable privacy controls, and unified international governance frameworks. Keywords: Artificial Intelligence, Data Privacy, Federated Learning, Differential Privacy, GDPR, Privacy-Enhancing Technologies, Homomorphic Encryption, Trustworthy AI