A Taxonomy of Data Risks in AI and Quantum Computing (QAI) - A Systematic Review

QAI systems face compounded risks: Quantum Artificial Intelligence combines vulnerabilities from both AI and quantum computing, creating "complex privacy and security vulnerabilities that are not systematically studied" until this review—meaning organizations deploying these systems may not fully understand the threats they face.

A comprehensive framework is needed: The researchers identified 22 distinct data risks organized into five categories (governance, risk assessment, control implementation, user considerations, and continuous monitoring), suggesting that current approaches to securing AI systems are incomplete and fragmented.

Governance and monitoring gaps exist: The taxonomy reveals significant vulnerabilities in how organizations oversee data risks, indicating that many companies lack adequate policies, oversight structures, and continuous monitoring systems to protect against emerging threats in AI and QAI systems.

Current risk assessment tools are insufficient: The study found "gaps in holistic risk assessment," meaning existing frameworks for evaluating AI safety and security don't adequately address the full spectrum of data risks—a critical gap for organizations trying to build trustworthy systems.

User-centered protections are underdeveloped: By identifying "user considerations" as a distinct risk category, the research highlights that safeguards protecting individual privacy and security in AI systems remain an underdeveloped area requiring urgent attention.