Which AI security by design option BEST mitigates targeted model poisoning and supply chain tampering?

Model poisoning attacks target the training data or model parameters to degrade performance or

introduce malicious behavior. Supply chain tampering introduces compromised components at

vendor or integration stages. Security by design principles require embedding defenses against these

threats from the earliest design stages.

Why C is Correct: According to ISACA AAIR security by design guidance, adversarial resilience and

data integrity controls address both model poisoning and supply chain tampering at their root.

Adversarial resilience training prepares the model to resist maliciously crafted inputs. Data integrity

controls—cryptographic signing, provenance tracking, integrity verification—detect tampering in

training data and model artifacts across the supply chain. Together, these form the most

comprehensive defense against both attack categories.

Why A is Wrong: Data refreshes with checksums detect post-hoc data corruption but do not build

adversarial resilience into the model itself. Checksums verify file integrity but cannot prevent

poisoning attacks that maintain file integrity while altering data content.

Why B is Wrong: Frequent retraining and bias monitoring address performance drift and fairness but

do not specifically protect against deliberate tampering. A retrained model may still be trained on

poisoned data if integrity controls are absent.

Why D is Wrong: Data tokenization protects sensitive field values from unauthorized access (a privacy

control) but does not address model poisoning or supply chain tampering, which can occur without

accessing or exposing the sensitive field values themselves.