AI Glossary
Data Poisoning
What is Data Poisoning?
A form of adversarial attack in which malicious data is deliberately introduced into a model's training set to manipulate its outputs. Data poisoning is a significant concern for models trained on web-scraped data or open contribution datasets. It is one reason model provenance and data governance matter in enterprise AI deployments.
Example in practice
A security researcher who injects subtly manipulated examples into a public dataset — causing any model trained on it to misclassify a specific input — has demonstrated a data poisoning attack on the supply chain.
Learn more
See Data Poisoning applied in a professional context through this free course.
AI Strategy for Leaders →