According to Ethan Mollick on X, concerns that all AI models exfiltrate peer‑review data are outdated, and journals should mandate enterprise accounts or models with training disabled to mitigate risk. As reported by Ethan Mollick citing a post from Max Kagan, the core risk from uploading a confidential manuscript to an LLM centers on data retention, model training, and vendor access controls, which are addressable via enterprise contracts, audit logs, and zero‑retention settings. According to Ethan Mollick, journals can set clear reviewer policies: require enterprise LLM tiers, disable training and logging for prompts, prohibit uploading identifiable author data, mandate prompt redaction, and require disclosure of any AI‑assisted review. As reported by Ethan Mollick, this approach balances confidentiality with productivity gains from structured critique, citation checks, and clarity rewrites, while preserving compliance for publishers and societies.