<ul><li>Data poisoning attacks the very heart of artificial intelligence systems by corrupting the dataset used to train machine learning or AI models.</li><li>Spotting data poisoning attacks requires carefully monitoring the model's accuracy and performance, looking for sudden changes, biased results, or unexpected outcomes.</li><li>Protecting against data poisoning involves implementing adversarial training, advanced data validation, and continuous monitoring of ML outputs.</li><li>Addressing the threat of data poisoning also requires educating teams about ML security and encouraging reporting of suspicious outcomes.</li></ul>

Guarding Against the Growing Threat of Data Poisoning

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