Aligning AI systems with human privacy preferences requires understanding individuals' nuanced disclosure behaviors beyond general privacy norms. Yet eliciting these boundaries remains challenging because they involve context-dependent trade-offs. We present an elicitation approach that probes individuals' privacy boundaries through a discriminative task. Through a between-subjects study with varying communication roles and delegation conditions, we collected 1,650 privacy boundaries from 169 participants across 60 scenarios. We examined how communication roles, AI delegation, and individual differences influence boundaries. Results show that communication roles significantly influence sensitivity to disclosure granularity or identifiability, depending on whose private information is shared; AI delegation and individuals' need for privacy heighten sensitivity to disclosed identifiers. Our analysis further reveals how AI delegation shifts privacy preferences across contexts, highlighting new complexities in AI-mediated communication. We advocate eliciting nuanced privacy boundaries within real-world data flows by considering roles and AI delegation, and enabling user involvement in guiding privacy-aware AI behavior.

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