An AI governance committee provides cross-functional oversight to align AI strategy, investment, and risk appetite with business goals. It sets policies, prioritizes portfolios, ensures accountability, and integrates compliance, ethics, and security into decision-making. While compliance, ethics, and information protection are essential, governance is the primary mechanism that systematically connects AI initiatives to enterprise objectives.

Direct feedback from users or stakeholders interacting with the AI application provides real-world insights into incorrect, harmful, or unexpected outputs. This makes it the most effective way to identify risks associated with outputs as they occur in actual use.

AAISM materials identify human-in-the-loop governance as the most effective safeguard against risks such as hallucinations in AI systems used in high-stakes domains like healthcare. By ensuring that human experts validate outputs before they influence patient treatment decisions, organizations preserve accountability, safety, and accuracy. Penetration testing is a cybersecurity measure, not relevant to hallucination risk. AI impact analysis helps evaluate systemic effects but does not directly prevent faulty outputs. Data validation improves input quality but cannot fully prevent generative hallucinations. The key safeguard is human-in-the- loop oversight.
References:
AAISM Study Guide - AI Governance and Program Management (Human Oversight in High-Risk AI) ISACA AI Security Management - Mitigating Hallucinations in Generative AI

AAISM emphasizes that the primary value of AI security risk assessments is prioritizing risks based on likelihood, impact, and business relevance.

Model cards document key details about an AI model, including its design, training data, performance, and limitations, providing the transparency and traceability needed to understand how the model operates and makes decisions.