What is one of the security risks specifically associated with AI?

Data poisoning is a significant security risk specifically associated with AI systems. This vulnerability occurs when adversaries intentionally introduce misleading or harmful data into the training dataset of an AI model. The aim is to manipulate the output of the AI, causing it to make incorrect predictions or decisions based on the corrupted data. Since AI systems heavily depend on the quality and integrity of the data used for training, even small amounts of poisoned data can lead to substantial performance degradation or harmful effects in deployment. By understanding data poisoning, organizations can implement stronger security measures and improve the robustness of their AI systems against such threats.

Other options, while they may pose risks in general data management or technology contexts, do not specifically tie to the unique vulnerabilities posed by AI systems. For example, bias in data interpretation relates more to ethical considerations and the potential for unfair outcomes rather than a direct security compromise. Low storage costs and hardware failure are also issues in the broader scope of technology and IT systems, but they do not carry the specific implications for security that data poisoning does within the context of AI. Thus, data poisoning distinctly captures a critical risk associated with the security of AI.