Imagine your AI learning from a sabotaged textbook!
Data poisoning sneaks false or manipulated information into the datasets that train AI and ML systems, turning once-smart models into unreliable tools.
Whether adding fake data, altering existing information, or deleting critical pieces, this attack undermines the foundation of AI-driven decision-making.
Organizations must prioritize clean, secure datasets and robust monitoring to shield their systems from such threats. After all, a solid AI starts with trusted data.
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