Not all artificially intelligent tools are built the same. One disparity that can make all the difference is whether a particular tool you and your team use is public or private.
Let’s dive into the distinction and why it matters so much.
AI has a wide range of business applications, arguably limited only by the user’s imagination and the quality of their prompt. Data can be organized and summarized, insights can be extracted, and ideas can be brainstormed. It is a highly useful productivity tool that can help your team accomplish far more with their time.
That said, it is crucial that you and your team are discerning about what you ask of (and especially what you give to) your AI tool of choice.
Why? Simple…
Some AI models are public, and some are private.
More specifically, public AI models use what you give them to “learn,” and will then reference your data in their responses to others. Meanwhile, private AI models rely on your internal data—not sharing it with others as part of the answers it generates.
As such, if your team members use a public AI to process sensitive or proprietary data, it constitutes a data breach. Imagine someone asking it to summarize John Doe’s medical history and identify the best medication combination for his conditions, to draft a business plan or marketing strategy, or to seek out patterns in proprietary data. Sharing any data with a public AI is effectively like posting it on Reddit, where anyone—from your competitors to cybercriminals to literally any query that's relevant enough—could access it.
That being said, there are ways that AI can be used responsibly for business purposes… you just have to be careful about it.
In order to most effectively use AI without actively inviting a data security issue, there are a few policies and precautions you should enact.
First, use a private, enterprise version whenever possible. The problem isn’t that public AI models will use your data to generate the optimal response; the issue lies in the fact that your data is then used to give everyone else the best response possible, too. One way to fix this is to invest in enterprise versions of these AI tools, which are specifically designed not to train on your data.
Second, draft and enforce an Acceptable Use Policy. For added safety, it certainly doesn’t hurt to establish boundaries and guidelines that your team must follow. Clearly define what can and what certainly can’t be provided to an AI platform in terms of your data.
We can help you identify the tool best suited to your needs and craft an AUP that matches your business’ requirements
Third, always double-check the AI’s work. This one is important because AI is not infallible. Boiled down, all Large Language Models/LLMs (like ChatGPT or Google Gemini) are effectively measuring the statistical probability of what word comes next—whether or not the output is factually correct. There are a few reasons this happens, from the comparative likelihood of certain words to data issues (whether the AI is relying on too little for the task at hand or has the opportunity to make connections where none exist).
Most concerningly, since AI is programmed to please, it may prioritize giving you any answer over giving you an accurate one. Inventing data or sources and contradictory statements are more common than you’d expect. Take your time, be careful and specific with your prompts, and fact-check what the AI spits out.
We can help you identify and manage the tools that work best for you. Give us a call at 571) 470-5594 to learn more about our business technology services.
