Conversely, proprietary LLMs typically offer robust security features but still pose data privacy and control risks. Using these models involves sharing sensitive data with a third-party provider, which could lead to regulatory penalties if a breach occurs.
LLMs also lack transparency regarding their training data and how datasets are formed. Be mindful of potential bias and fairness issues and consider a human-in-the-loop approach, where specialists review and manage the model’s output.
LLMs are most effective when used to streamline complex processes and drive innovation. To leverage these models responsibly, prioritize data governance—especially in highly regulated industries.
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