An organization is assessing the risks of using a foundation model fine-tuned on proprietary data for customer interactions. Which emerging risk is MOST specific to fine-tuned models compared to using the base foundation model?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — fine-tuning on your proprietary data raises the chance the model memorizes sensitive details and leaks them in outputs. That data-exposure risk is specific to the fine-tuning process, not just using the base model.
Full Explanation
Fine-tuning on proprietary data increases the risk that the model memorizes sensitive information from that dataset, which can be extracted through targeted queries or surface unexpectedly in responses. That data-exposure risk is specific to fine-tuning. The claim that inference requires more compute than the base model is not universally true. The claim that it cannot be used for multilingual customer interactions is not an inherent limitation. The claim that fine-tuning is exempt from the EU AI Act is incorrect; fine-tuned models remain within scope.