To mitigate data risks, enterprises should bring LLMs close to their data and operate within their existing security perimeter. This means hosting and deploying LLMs within a protected environment, allowing teams to customize and interact with the models securely. By doing so, businesses can balance innovation with the need to protect sensitive information.

Enterprises are concerned that publicly hosted LLMs may learn from their prompts and inadvertently disclose proprietary information. There are also risks of sensitive data being stored online, which could be exposed to hackers or made public. These factors make it risky for businesses, especially those in regulated industries, to use publicly hosted LLMs.

Companies can customize LLMs by fine-tuning them with internal data that they trust. This involves using models that can be downloaded and adapted for specific use cases, allowing businesses to generate more relevant insights. By focusing on smaller, targeted models, organizations can also reduce resource needs and operate more efficiently.