Failure containment is the design practice of limiting the blast radius when an AI system makes a mistake, gets manipulated, or behaves unpredictably. Instead of letting one bad decision spread through a workflow, the system is built so errors stop at a narrow boundary and do not cascade into higher-risk actions.
This matters in cyber security because autonomous or tool-using AI can move from generating text to taking actions: sending requests, changing records, triggering scripts, or sharing data. If the model is tricked by prompt injection, confused by ambiguous input, or simply fails under edge cases, containment controls help prevent damage. Common defenses include least privilege, approval gates for sensitive steps, action logging, rate limits, sandboxing, and safe fallback states. In practice, failure containment turns AI from a single point of escalation into a monitored component with bounded impact.