The Risk of Sharing Sensitive Data with Public AI Platforms
As employees and students embrace generative AI platforms, IT professionals must find ways to ensure that sensitive data isn’t being shared publicly. Users need ways to explore large language models (LLMs) without disclosing any of their data.
“First, we do a data governance check. What kind of data are you going to be using? What are the controls around that data? Then we can design a solution that allows you to keep your data in-house and not expose any data,” says Roger Haney, chief architect for software-defined infrastructure at CDW.
Data governance is key for organizations looking to prepare their infrastructure and users for AI and LLMs.
“We have a workshop called Mastering Operational AI Transformation, or MOAT,” Haney says. “You’re drawing a circle around the data that we don’t want to get out. We want it to be internally useful, but we don’t want it to get out.”
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To ensure data security, partners such as CDW can help organizations set up or build cloud solutions that don’t rely on public LLMs. This gives them the benefits of generative AI without the risk.
“We can set up your cloud in a way where we’re able to use a prompt to a make copy of an LLM,” Haney explains. “We build private enclaves containing a chat resource to an LLM that people can use without a public LLM learning the data they’re putting in.”
When to Host AI Databases in the Cloud
Organizations’ plans for generative AI will determine how they should prepare their infrastructure for the future of this technology. Haney says most users want to communicate with their data for retrieval or analysis purposes.
“Chatting with your data doesn’t require a new data store. You don’t have to build a huge data lake or warehouse,” he says. “If you have student data, then we add another model that can create the query in SQL, do the query and pull the data back. Then you can ask it questions, using that data as part of your prompt, and you can talk with your data.”