A Generative AI Engineer is deploying a customer-facing, fine-tuned LLM on their public website.

Model inversion attacks occur when an attacker uses the model's outputs to reconstruct the sensitive training data used during the fine-tuning process. To mitigate this in a public-facing application, implementingAI Guardrailsis the most relevant strategy. Guardrails act as a programmable "filter" between the LLM and the end-user. They can be configured to detect if a model's response contains patterns that look like proprietary training data or PII (Personally Identifiable Information). While ACLs (B) and ABAC (D) protect the model'sinfrastructure(who can invoke the API), they do not inspect thecontentof the output, which is where the inversion attack actually manifests. Databricks provides integrated guardrail capabilities (via Mosaic AI Gateway) specifically to enforce compliance and prevent the leakage of sensitive internal knowledge that may have been baked into the model weights during fine-tuning.