A cloud provider offers a managed large language model that a customer consumes purely through an inference API, sending prompts and receiving completions. Under the AI shared responsibility model, which security obligation belongs to the customer rather than the provider?
- AHardening the GPU host operating system and hypervisor that execute the model weights
- BPatching the model training pipeline and retraining framework libraries
- CApplying differential privacy to the corpus used to pretrain the foundation model
- DGoverning what data is placed into prompts and validating the model output before acting on it Correct
Why A is wrong: This is tempting because model inference is compute-intensive and the customer pays for that compute, but in a managed inference service the provider owns the underlying host, hypervisor and accelerators, so this is provider responsibility.
Why B is wrong: This sounds correct because vulnerable libraries are a real risk, but the customer using only an inference API does not operate the training pipeline; the provider builds and maintains the managed model, so this sits with the provider.
Why C is wrong: Privacy of the training corpus is a genuine concern, but the pretraining corpus and its privacy controls belong to the provider that trained the managed model, not to a customer that only calls inference.
Why D is correct: In a consumption model the customer controls the inputs it sends and the use it makes of the outputs, so classifying prompt data, preventing sensitive data leakage into prompts, and validating completions are the customer's responsibility.