An AI model serving endpoint has been identified as vulnerable to a model inversion attack because it returns detailed confidence scores for all output classes in its API response. The engineering team must reduce this risk while maintaining utility for authorized callers. What is the most appropriate mitigation?

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A. Return only the top prediction label and its confidence score rather than full probability distributions for all classes, and implement differential privacy on outputs for additional protection. Reducing the information density of API responses — returning only top-label confidence rather than full probability distributions — directly limits the information available to conduct model inversion, while differential privacy adds mathematical privacy guarantees. The SecAI+ objectives include mitigating adversarial risks to inference layers. Complete shutdown eliminates utility. SQL injection protections are irrelevant to model inversion. Air-gapping with no API access eliminates the service.

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