When designing an AI security architecture, what is the PRIMARY purpose of using adversarial training?

The primary purpose of adversarial training is to make AI models more resilient to attacks by generating adversarial examples - inputs perturbed specifically to mislead the model - and including them in the training dataset alongside correct labels. This teaches the model to maintain correct behavior even when inputs are deliberately manipulated by an attacker. Adversarial training is a well-established defense against adversarial example attacks and is critical in security-sensitive AI deployments.

Improving fairness in AI decisions is achieved through debiasing techniques and representative data curation, not through adversarial training which targets attack resilience.

Adversarial training increases computational cost because it requires generating and processing additional adversarial samples during training - it does not reduce the cost of defense.

Adversarial training focuses on hardening the model against manipulated inputs; standard dataset performance is already the objective of normal training and is not the target of adversarial methods.