Within the ISACA Advanced in AI Risk framework, life-cycle controls should protect data quality, model design, testing, validation, monitoring, change management, and secure retirement of AI systems. Secure coding throughout the AI development lifecycle reduces exploitable software and integration vulnerabilities before deployment. It does not directly detect model drift, eliminate human review, or guarantee predictive accuracy. This makes option B, Fewer model vulnerabilities that can be exploited by malicious actors, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.