A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false…

The correct answer is D. Insufficient model validation and change control processes. This scenario demonstrates insufficient model validation and change control processes (D). Disabling risk signals is a configuration change to the model's behavior. Robust change control would require impact assessment, testing, and sign-off before such changes are made…

AI Security Assurance and Resilience

Question

A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?

Options

ALack of sufficient computing resources for the AI system
BExcessive reliance on external consultants for model design
CAbsence of metrics and dashboards for analysts
DInsufficient model validation and change control processes

How the community answered

(55 responses)

A
16% (9)
B
7% (4)
C
4% (2)
D
73% (40)

Explanation

This scenario demonstrates insufficient model validation and change control processes (D). Disabling risk signals is a configuration change to the model's behavior. Robust change control would require impact assessment, testing, and sign-off before such changes are made - ensuring that reducing false positives does not unacceptably increase false negatives (missed fraud). Computing resources (A) and consultant reliance (B) are unrelated to this incident. Absent dashboards (C) might obscure the problem but don't explain why the change was allowed to proceed without proper validation.

Topics

#AI Governance#Model Validation#Change Control#AI Risk Management

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