Navigating AI's Data Minefield: Technical Risks in the Design and Development Stage

This article delves into the critical technical risks associated with data in the AI model development lifecycle, a crucial area for internal audit and assurance professionals. It highlights how issues like training-data bias, poor data quality, unrepresentative data, data leakage, and data poisoning can silently undermine AI model performance and lead to significant business and regulatory exposure. Understanding these risks is paramount for auditors to effectively assess the reliability and integrity of AI systems from their foundational data.