Untrustworthy AI: How to deal with data poisoning

This article examines risks of data poisoning in AI/ML systems, detailing attack types (data injection, insider abuse, trigger injection, and supply-chain compromises) and their potential to distort model outputs and erode trust. It advises preventive measures—continuous dataset integrity checks, security-by-design practices, adversarial training, and zero-trust access management—to strengthen the resilience of AI platforms.