Artificial intelligence (AI) is becoming more common in businesses and simultaneously the adversarial AI attacks are also becoming a serious threat. The attacks target weaknesses in AI models and are growing in frequency as well as in sophistication.
Recent reports paint a concerning picture. A study by HiddenLayer found that 77% of companies have experienced AI-related security issues. Meanwhile, Gartner’s survey showed that 41% of businesses reported AI security incidents such as adversarial attacks on machine learning (ML) models. The incidents will rise as more companies adopt AI technologies. As of now, 73% of organizations are using hundreds or thousands of AI models.
Adversarial AI attacks work by manipulating the inputs to AI systems. Attackers can introduce corrupted data or hidden commands to make AI systems misbehave. Slight changes in an image or input can trick AI models into giving incorrect predictions. A self-driving car once misinterpreted a stop sign as a yield sign due to small stickers placed by attackers.
The risk does not stop at misclassifications. The attacks can lead to data breaches, financial losses and public safety risks in industries like healthcare, finance and autonomous driving. Adversarial attacks could manipulate AI systems in healthcare that analyze patient data. It can lead to incorrect diagnoses or treatments.
Businesses need to take proactive measures to defend against the growing threats. Adversarial training is one way to boost resilience. Securing the data pipelines that feed into AI systems is also important. Regular audits of AI models, monitoring for unusual behavior and strengthening API security can further protect systems from exploitation.
As AI continues to play a bigger role in business operations, the threat of adversarial attacks will only increase. It is not a matter of if but when an organization will face such attacks.
