Authors: Associate Professor Dr. Surender Singh
Abstract: Artificial Intelligence (AI) systems have become essential across industries, supporting decision-making, automation, healthcare, finance, and cybersecurity. However, the increasing complexity of AI pipelines introduces significant risks related to data integrity, model drift, security vulnerabilities, regulatory compliance, and operational failures. Embedding governance within AI pipelines provides a structured framework to ensure accountability, transparency, recoverability, and resilience. This paper examines governance mechanisms integrated throughout the AI lifecycle and demonstrates how embedding governance enhances system recovery while minimizing operational, ethical, and security risks. The study proposes a governance-driven AI pipeline architecture incorporating continuous monitoring, version control, audit trails, explainability, and automated rollback mechanisms. The findings indicate that governance significantly improves reliability, trustworthiness, and regulatory compliance while reducing downtime and model-related failures.
