Which of the following controls would BEST help to prevent data poisoning in AI models?

The correct answer is B. Implementing a strict data validation mechanism. Data poisoning attacks inject corrupted, adversarially crafted, or malicious records into the training pipeline to manipulate model behavior. Strict data validation - including schema enforcement, anomaly detection, provenance checks, and integrity verification - intercepts and…

AI Security Design and Implementation

Question

Options

AIncreasing the size of the training data set
BImplementing a strict data validation mechanism
CEstablishing continuous monitoring
DRegularly updating the foundational model

How the community answered

(36 responses)

A
3% (1)
B
83% (30)
C
6% (2)
D
8% (3)

Explanation

Data poisoning attacks inject corrupted, adversarially crafted, or malicious records into the training pipeline to manipulate model behavior. Strict data validation - including schema enforcement, anomaly detection, provenance checks, and integrity verification - intercepts and rejects malicious inputs before they can corrupt the training dataset. Increasing dataset size (A) dilutes but does not eliminate poisoned samples. Continuous monitoring (C) may detect degraded performance after the fact but doesn't prevent poisoning at ingestion. Updating the foundational model (D) doesn't protect against future poisoning attempts.

Topics

#Data poisoning prevention#AI security controls#Data validation#AI model integrity

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