Which of the following BEST reduces the risk of exposing sensitive data through the output of large language models (LLMs) in applications?

The correct answer is C. Implementing data sanitization techniques. AAISM materials make clear that the best safeguard against sensitive information being leaked through the outputs of LLMs is data sanitization. This involves filtering, redacting, or masking sensitive content before the model can use it, thereby preventing unintended disclosure…

AI Security Design and Implementation

Question

Options

AEncrypting data in transit and at rest
BConducting adversarial testing
CImplementing data sanitization techniques
DEnforcing least privilege access

How the community answered

(25 responses)

A
8% (2)
B
4% (1)
C
72% (18)
D
16% (4)

Explanation

AAISM materials make clear that the best safeguard against sensitive information being leaked through the outputs of LLMs is data sanitization. This involves filtering, redacting, or masking sensitive content before the model can use it, thereby preventing unintended disclosure in outputs. Encryption protects confidentiality in storage and transmission but does not stop output leaks. Adversarial testing helps identify vulnerabilities but does not prevent exposure by itself. Least privilege access restricts who can interact with the model but does not sanitize the content of its outputs. The control most directly tied to preventing leakage is implementing data sanitization techniques.

Topics

#LLM data security#Data sanitization#Sensitive data protection#AI output security

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