🤖 AI Summary
AI systems—characterized by autonomous learning and opaque decision-making—pose escalating privacy risks that conventional privacy frameworks struggle to address. This study conducts a systematic literature review of 45 peer-reviewed works to develop, for the first time, a comprehensive, four-dimensional classification framework for AI privacy risks, integrating technical and human factors across datasets, models, infrastructure, and insider threats. The framework identifies 19 distinct risk categories; human error emerges as the predominant root cause (9.45% prevalence), with risk distribution relatively balanced across dimensions. By explicitly incorporating human behavior—an aspect largely overlooked in prior taxonomies—this work bridges a critical gap in AI privacy research. It provides empirically grounded foundations and actionable governance pathways for advancing trustworthy AI development and deployment.
📝 Abstract
Artificial Intelligence (AI) systems introduce unprecedented privacy challenges as they process increasingly sensitive data. Traditional privacy frameworks prove inadequate for AI technologies due to unique characteristics such as autonomous learning and black-box decision-making. This paper presents a taxonomy classifying AI privacy risks, synthesised from 45 studies identified through systematic review. We identify 19 key risks grouped under four categories: Dataset-Level, Model-Level, Infrastructure-Level, and Insider Threat Risks. Findings reveal a balanced distribution across these dimensions, with human error (9.45%) emerging as the most significant factor. This taxonomy challenges conventional security approaches that typically prioritise technical controls over human factors, highlighting gaps in holistic understanding. By bridging technical and behavioural dimensions of AI privacy, this paper contributes to advancing trustworthy AI development and provides a foundation for future research.