Fortify AI Training Datasets From Malicious Poisoning

This commentary warns about the growing risk of AI data poisoning—where attackers inject deceptive or malicious data into AI training sets to subvert outputs—and outlines defensive steps organizations can take, including maintaining a live data catalog, establishing baselines for user and device behavior, monitoring access and changes to training data, and enforcing governance policies to preserve data integrity.