<![CDATA[How are you handling prompt injection defense in AI applications?]]>

<![CDATA[How are you handling prompt injection defense in AI applications?]]>As AI applications become more connected to enterprise data and workflows, prompt injection is becoming an important security challenge. A malicious instruction can potentially influence an AI system to bypass intended behavior, expose sensitive information, or trigger unintended actions.

What approaches are teams using for prompt injection defense? Are techniques like input validation, access controls, output filtering, monitoring, and red-team testing proving effective in production?

Would be interested to hear what security practices others are implementing to protect AI applications against prompt injection attacks.

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