Human–AI Co-thinking for Verification Under Cognitive Risk: A Strongly Anonymized Case Study (KEI Case)

Generative AI systems are increasingly used not only for information retrieval but also for reasoning support. However, high-risk interactions can emerge when conversational dynamics reduce verifiability through buffering, delay, or ambiguity, thereby weakening responsibility mapping and auditability. We present a strongly anonymized case study (“KEI Case”) in which a user experienced an incident that could not be reliably organized through interpretation alone.Rather than engaging in emotional narrative or accusatory framing, the user positioned the AI as a co-thinker to transform the incident into a submission-grade, regulator-facing verification package. We formalize a practical workflow—Evidence Freeze, Timechain, Fit Mapping, Verification Q-set, Required Material List (RML), and Annex Evidence Pack—and propose a modular behavioural taxonomy (A–F) to generate verifiable questions without concluding intent. We discuss implications for auditability, cognitive sovereignty, and governance-oriented verification under emerging regulatory regimes, and outline design recommendations for “verification-first” co-thinking interactions.