{"id":3238,"date":"2024-04-24T08:09:59","date_gmt":"2024-04-24T13:09:59","guid":{"rendered":"https:\/\/www.darkreading.com\/cybersecurity-operations\/fortify-ai-training-datasets-from-malicious-poisoning"},"modified":"2024-04-24T08:09:59","modified_gmt":"2024-04-24T13:09:59","slug":"fortify-ai-training-datasets-from-malicious-poisoning","status":"publish","type":"post","link":"https:\/\/ddi.mohflo.net\/index.php\/2024\/04\/24\/fortify-ai-training-datasets-from-malicious-poisoning\/","title":{"rendered":"Fortify AI Training Datasets From Malicious Poisoning"},"content":{"rendered":"
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COMMENTARY<\/span><\/span><\/p>\n

Picture this: It’s a Saturday morning, and you made breakfast for your family. The pancakes were golden brown and seemingly tasted OK, but everyone, including you, got sick shortly after eating them. Unbeknown to you, the milk that you used to make the batter expired several weeks ago. The quality of the ingredients impacted the meal, but everything looked fine on the outside.<\/span><\/p>\n

The same philosophy can be applied to artificial intelligence (AI). Regardless of its purpose, AI’s output is directly related to the quality of its input. As the popularity of AI continues to rise, security concerns around the data being fed into AI are coming into question.<\/span><\/p>\n

A majority of today’s organizations are integrating AI into business operations at some capacity \u2014 and threat actors are taking note. Over the past few years, a tactic known as AI poisoning has become increasingly prevalent. This new malicious practice involves injecting deceptive or harmful data into AI training sets. The tricky part about AI poisoning is that, despite the input being compromised, the output can initially continue as normal. It isn’t until a threat actor gets a firm grip on the data and begins a full-fledged attack that deviations from the norm become obvious. The consequences range from slightly inconvenient to damaging a brand’s reputation.<\/span><\/p>\n

It’s a risk affecting organizations of all sizes, even today’s most prominent tech vendors. For example, over the past few years, adversaries launched several large-scale attacks to poison Google’s Gmail spam filters and even turned Microsoft’s Twitter chatbot hostile.<\/span><\/p>\n

Defending Against AI Data Poisoning<\/h2>\n

Fortunately, organizations can take the following steps to shield AI technologies from potential poisoning.<\/span><\/p>\n

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