Almost anyone can poison a machine learning (ML) dataset to alter its behavior and output substantially and permanently. With careful, proactive detection efforts, organizations could retain weeks, months, or even years of work they would otherwise use to undo the damage that poisoned data sources caused.
Data poisoning is a type of adversarial ML attack that maliciously tampers with datasets to mislead or confuse the model. The goal is to make it respond inaccurately or behave in unintended ways. Realistically, this threat could harm the future of AI. As AI adoption expands, data poisoning becomes more common. Model hallucinations, inappropriate responses, and misclassifications caused by intentional manipulation have increased in frequency. Public trust is already degrading — only 34% of people strongly believe they can trust technology companies with AI governance.
