The integrity of data fed into AI systems is a critical concern. The AAIA™ Study Guide emphasizes that validation and filtration processes are essential to mitigate the risk of data poisoning—an attack that can manipulate model behavior by injecting malicious inputs.

“Data poisoning represents a major vulnerability in AI pipelines. Effective controls include robust validation, filtration, and monitoring of training data sources. These preventive practices are essential to ensure model reliability and security.”

While options A, B, and C are important operational and training measures, only D addresses a technical risk that can directly compromise model outputs and trustworthiness.