Data Integrity as a Code (DIaC) Framework for Securing Banking AI/ML Systems

Abstract

As the banking industry embraces the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) systems, ensuring data integrity and security becomes paramount. Data Integrity as a Code (DIaC) framework emerges as a comprehensive approach to safeguarding AI/ML systems in banking. DIaC integrates data governance, validation, encryption, and continuous monitoring into the very fabric of AI/ML development, mitigating risks associated with data breaches, model manipulation, and algorithmic bias. This paper presents the DIaC framework and explores its application in securing banking AI/ML systems, fostering trust, compliance, and resilience in the dynamic digital banking landscape.