GCP Professional Cloud Security Engineer Practice Question
Your team is releasing a customer-facing chat assistant that uses Vertex AI's generative text model. To answer questions the application sometimes includes internal documents in the system prompt. Security testing shows that a skilled user can craft prompts that make the model echo confidential data from those documents. Which Google Cloud control most directly reduces this adversarial prompt-injection risk without changing the model weights or retraining?
Place the Vertex AI endpoint inside a VPC Service Controls service perimeter.
Encrypt all training data with customer-managed encryption keys (CMEK) before fine-tuning the model.
Call Sensitive Data Protection to inspect and redact the model's response before it is sent back to the user.
Enable CMEK on the Vertex AI model to ensure its checkpoints are stored with customer-managed keys.
Adversarial or "jailbreak" prompts may trick a large language model into disclosing data that should stay private. By placing a Sensitive Data Protection (SDP) inspection call immediately before the response is returned, the application can automatically detect and redact or block any text that contains PII or other definable confidential content. This mitigates leakage even if the prompt successfully manipulated the model. Enabling CMEK, encrypting training data, or isolating the service in a VPC perimeter are valuable controls for other threats, but none of them prevent the model from including sensitive strings in its generated output.
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What is Sensitive Data Protection (SDP) in Google Cloud?
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How does SDP mitigate adversarial prompt-injection risks?
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Why don't CMEK or VPC Service Controls help against adversarial prompt injection?
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What is Sensitive Data Protection (SDP) in Google Cloud?
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How does SDP protect against adversarial prompt injection in Vertex AI?