Data poisoning poses a significant threat to AI system integrity, where maliciously manipulated training data can lead to incorrect predictions and systemic vulnerabilities.
As AI systems increasingly depend on extensive data, the risks of data poisoning have been amplified by factors like massive data collection and automated pipelines.
Complex AI models, particularly deep learning architectures, are primarily at risk due to their intricate nature and the black-box opacity that conceals data manipulation effects.
With the integration of AI into high-stakes applications, data poisoning's potential to disrupt critical systems heightens the urgency for robust security measures.
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