Model containment is the strategic practice of governing AI models by isolating them within defined operational boundaries. It ensures security, compliance, and reliability by preventing unintended actions or data exposure. Used by enterprises deploying LLMs, it benefits data scientists, risk officers, and developers through controlled testing and deployment. This framework mitigates hallucinations and bias, fostering trustworthy, scalable AI integration across industries.
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