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AI Risk Program ManagementmediumMultiple ChoiceObjective-mapped

AAIR AI Risk Program Management Practice Question

You are establishing an AI risk appetite framework using the NIST AI RMF. Which action best ensures alignment between AI innovation velocity and organizational risk tolerance?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Define risk tolerance levels based on potential impact to human rights and safety, differentiated by model deployment context.

Aligning risk appetite requires mapping technical AI performance metrics to business-level risk registers, ensuring that performance trade-offs are explicitly approved by stakeholders.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Set a static threshold for model accuracy at 95% across all AI projects.

    Why it's wrong here

    Static thresholds fail to account for varying risk profiles across different use cases.

  • Define risk tolerance levels based on potential impact to human rights and safety, differentiated by model deployment context.

    Why this is correct

    Differentiating by context is a core requirement of the NIST AI RMF for tailored risk management.

  • Require all AI teams to use exclusively open-source models to mitigate vendor-related risks.

    Why it's wrong here

    This is a mitigation strategy, not a definition of risk appetite.

  • Delegate all AI risk acceptance decisions to the lead data scientist.

    Why it's wrong here

    Risk acceptance is a business governance function, not a technical one.

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Written and reviewed by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

Last reviewed August 2026 · checked against the official ISACA exam blueprint

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