Free Isaca AAISM Actual Exam Questions - Question 8 Discussion

Option B could also be a strong choice because applying differential privacy and masking sensitive patterns can reduce the chance that the model learns specific hidden triggers in the first place. If those triggers are related to sensitive patterns in training data, this approach helps prevent them from being encoded. This is more of a preventative measure compared to D’s reactive monitoring and can complement adversarial training (C) by addressing vulnerabilities during data preparation. So, focusing on data privacy and masking might cut down hidden triggers before they even get introduced.

Probably A. Regular retraining with diverse data can reduce hidden triggers by constantly updating the model’s knowledge and diluting any malicious patterns embedded earlier. It’s more proactive than just monitoring outputs.

Maybe D makes sense here since it focuses on catching triggers in action rather than just trying to prevent them upfront. If a hidden trigger pops up, monitoring outputs and spotting suspicious patterns could help catch things that slipped through training defenses. It’s more reactive but practical for ongoing risk, especially if new triggers appear after deployment. The others are more about prevention, but some triggers might only be noticeable once the model is live and generating outputs. So keeping an eye on what the model actually does might be the best way to spot hidden triggers early.

Option C seems stronger since it targets triggers during training, not just after deployment.

It’s B because masking sensitive data stops triggers from being embedded during training itself.

It’s A because regularly retraining with diverse data helps the model forget or override hidden triggers, making it less likely those vulnerabilities persist over time compared to just reacting after the fact.

C, because preventing triggers during training is stronger than just spotting them later.

It’s C. Adversarial training actively tries to uncover and neutralize hidden triggers before the model even goes live, which feels like a more direct approach than just monitoring outputs after deployment (D). Plus, retraining with diverse data (A) is good but doesn’t specifically target those sneaky triggers. Differential privacy (B) focuses more on protecting sensitive info rather than spotting or fixing these hidden traps. So, C seems best for actually addressing the vulnerabilities head-on during the training process.

B tbh sounds like a solid choice here too. Applying differential privacy and masking sensitive patterns in training data can reduce the chance that those hidden triggers get learned in the first place. It’s more about preventing the model from picking up on any suspicious or sensitive info that might be exploited later. While adversarial training (C) tries to find triggers, B aims to limit what the model even sees that could include them, which is a strong preventive step before you even get to testing or deployment.

Option A sounds good because retraining with diverse data can reduce biases and hidden triggers by covering more scenarios, making the model less likely to react unexpectedly.

It’s D for me. While adversarial training is proactive, monitoring outputs lets you catch any hidden triggers slipping through in real-time. That ongoing detection is crucial since no training method can guarantee all triggers are neutralized beforehand. You need a way to spot suspicious patterns as they happen, so you can respond quickly and adjust if necessary.

I’m leaning towards C because adversarial training specifically targets hidden triggers by trying to expose them during training, which seems like the best way to neutralize vulnerabilities before deployment. What do you all think?