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 # Why Red Team AI Systems?
 
-AI systems introduce novel risks beyond traditional software, such as emergent unintended capabilities, complex failure modes, susceptibility to subtle data manipulations, and potential for large-scale misuse (e.g., generating disinformation). AI Red Teaming is necessary because standard testing methods often fail to uncover these unique AI vulnerabilities. It provides critical, adversary-focused insights needed to build genuinely safe, reliable, and secure AI before deployment.
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-Learn more from the following resources:
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-@article@What's the Difference Between Traditional Red-Teaming and AI Red-Teaming? - Cranium AI - Compares objectives, techniques, expertise, and attack vectors to highlight why AI needs specialized red teaming.
-@article@What is AI Red Teaming? The Complete Guide - Mindgard - Details specific use cases like identifying bias, ensuring resilience against AI-specific attacks, testing data privacy, and aligning with regulations.
-@article@The Expanding Role of Red Teaming in Defending AI Systems - Protect AI - Explains why the dynamic, adaptive, and often opaque nature of AI necessitates red teaming beyond traditional approaches.
-@article@How red teaming helps safeguard the infrastructure behind AI models - IBM - Focuses on unique AI risks like model IP theft, open-source vulnerabilities, and excessive agency that red teaming addresses.
+AI systems introduce novel risks beyond traditional software, such as emergent unintended capabilities, complex failure modes, susceptibility to subtle data manipulations, and potential for large-scale misuse (e.g., generating disinformation). AI Red Teaming is necessary because standard testing methods often fail to uncover these unique AI vulnerabilities. It provides critical, adversary-focused insights needed to build genuinely safe, reliable, and secure AI before deployment.