In AI governance, what is essential when engaging AI vendors?

• A. Adapting vendor management programs to include AI-specific risks
• B. Treating all vendors the same regardless of AI involvement
• C. Avoiding any form of risk assessment with vendors
• D. Segmenting AI vendors but ignoring risks

Answer: (A)

Engaging AI vendors requires embedding AI-specific risk considerations into vendor management. AI systems bring unique challenges around data handling, training data provenance, model behavior and drift, security of deployment, and accountability for outcomes. To manage these risks, the vendor-management program should require AI risk assessments, clear data governance terms, and controls in contracts—covering data usage, retention, privacy, model security, testing, monitoring, and the right to audit or terminate if issues arise. Ongoing monitoring is needed as models evolve with updates or retraining, and there should be a plan for incident response and change management. This integrated approach ensures vendors align with governance policies and regulatory requirements, not leaving AI risk unaddressed. Treating all vendors the same, avoiding risk assessments, or ignoring risks would leave gaps where AI-specific risks could lead to privacy breaches, biased outcomes, or control failures.