AAISM Domain 1 AI Governance, Program Management Practice Test

Deliberately feeding incorrect data to an AI to produce incorrect results is data poisoning. The idea is to taint the information the model learns from, so its outputs become unreliable, biased, or aligned with the attacker’s goals. This typically happens during training or data collection, when manipulated data shifts the model’s understanding or creates vulnerabilities that show up later in deployment. Since the attack targets the model’s learned parameters and knowledge, the effects can persist and be hard to undo without remediation like data cleansing or retraining. Prompt injection, in contrast, aims to influence the model’s behavior at inference time through the prompt itself, not by altering the training data. Model drift refers to natural changes in data patterns over time that affect performance, not deliberate manipulation. Adversarial inference isn’t the standard term for this kind of data manipulation and is not the right descriptor for feeding bad data to corrupt training.