AAIR AI Risk Governance And Framework Integration Practice Question
An organization is integrating AI risk into its existing ISO 31000 framework. How should the 'Risk Assessment' process be modified to account for AI-specific 'black box' issues?
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
✓
Add a model explainability and interpretability assessment step to the process.
Inclusion of model explainability assessments is necessary to address the opacity inherent in deep learning models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the risk identification step, as AI risks are too unpredictable.
Why it's wrong here
Risk identification is the foundation of the ISO 31000 framework.
- ✗
Replace the qualitative assessment with a purely quantitative financial impact model.
Why it's wrong here
AI risk is often qualitative; financial models alone fail to capture ethical/reputational risks.
- ✓
Add a model explainability and interpretability assessment step to the process.
Why this is correct
Explainability is a key AI risk control that ensures transparency in decision-making.
- ✗
Delegate all risk assessments to the external software vendor.
Why it's wrong here
Delegating responsibility does not absolve the organization of its internal risk governance.
About these practice questions
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JA
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
This AAIR practice question is part of Courseiva's free ISACA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AAIR exam.