CRISC Information Technology and Security Practice Question
A financial institution is adopting AI for credit scoring. The model is currently a black box and requires explainability for regulatory compliance. Which risk is MOST critical to address?
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
✓
Lack of explainability
Regulated decisions require explainability. If the AI model cannot provide explanations, the institution risks regulatory non-compliance and potential legal challenges.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model bias
Why it's wrong here
Bias is important but can be addressed without full explainability; however, regulatory requirements specifically mandate explainability.
- ✗
Adversarial attacks
Why it's wrong here
Adversarial attacks are a security concern but not the most critical for regulatory compliance.
- ✓
Lack of explainability
Why this is correct
Explainability is required by regulations for credit decisions; lack thereof can lead to non-compliance.
- ✗
Data privacy in training
Why it's wrong here
Data privacy is important but explainability is the immediate regulatory requirement.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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