Containment Failures

Containment failures refer to cases where an AI system acts outside the boundaries its operators intended, such as accessing data, tools, or systems it was never meant to reach, or continuing to act after it should have been stopped. For enterprise governance teams, these incidents matter because they show that access controls, sandboxing, and human override mechanisms may not hold under real conditions. Tracking them helps compliance and risk leaders judge whether an AI deployment's safeguards are adequate before a failure creates legal exposure or harms customers.

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