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Unravelling the threat of data poisoning to generative AI

The rise of generative AI tools is set to become an essential part of everyday work life in 2024. However, this also brings new challenges in cybersecurity, particularly in dealing with data poisoning attacks. Data poisoning occurs when bad actors manipulate training data to compromise the performance and output of AI models. These attacks can be difficult to detect and correct, making it crucial for organizations to implement proactive measures to safeguard their AI systems. This includes ensuring clean and unmanipulated training data, employing statistical methods to detect anomalies, controlling access to data sets, and continuously monitoring model performance.

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