Which practice is essential for AI project governance according to CPMAI?

Which practice is essential for AI project governance according to CPMAI?

• A. Continuous monitoring of model drift
• B. Solely documenting processes
• C. Disregarding data privacy
• D. Deploying without monitoring

Answer: (A)

In CPMAI governance, keeping the model aligned with the real world over time is essential, and that comes from continuously monitoring for drift. Data drift happens when the input data’s statistics change, and concept drift occurs when the relationship between inputs and the target variable shifts. If you don’t track these shifts, the model can silently become less accurate, biased, or unsafe, even if it performed well during development. Ongoing monitoring provides alerts, triggers retraining or validation actions, and creates an auditable trail of performance and drift events, which supports accountability, regulatory compliance, and responsible risk management. Documentation, privacy considerations, and careful deployment are important, but they don’t by themselves ensure ongoing governance—the active, ongoing observation of how the model behaves in production is what keeps the project trustworthy and effective.

In CPMAI governance, keeping the model aligned with the real world over time is essential, and that comes from continuously monitoring for drift. Data drift happens when the input data’s statistics change, and concept drift occurs when the relationship between inputs and the target variable shifts. If you don’t track these shifts, the model can silently become less accurate, biased, or unsafe, even if it performed well during development. Ongoing monitoring provides alerts, triggers retraining or validation actions, and creates an auditable trail of performance and drift events, which supports accountability, regulatory compliance, and responsible risk management. Documentation, privacy considerations, and careful deployment are important, but they don’t by themselves ensure ongoing governance—the active, ongoing observation of how the model behaves in production is what keeps the project trustworthy and effective.