As more businesses continue to adopt ChatGPT and other generative AI tools leveraging large language models, maintaining data privacy becomes critical. After all, at their core, these emerging applications essentially function as random word generators. So sufficient care must be taken to prevent trade secrets or other private information from being shared by an advanced chatbot.
Of course, organizations with strong regulatory requirements – governmental agencies, financial and investment businesses, etc. – need an even more rigorous approach to data privacy when implementing generative AI applications using large language models. This is especially the case when trying to craft machine learning powered software without sufficient experience in the practice. In nearly all cases, partnering with digital agencies with expertise in building these types of applications becomes paramount.
So let’s look more closely at the critical nature of data privacy when building generative AI applications or even implementing something like ChatGPT. Remember, being an early adopter of any emerging tech innovation requires a measured approach. Once again, finding a partner with the technical chops, expertise, and entrepreneurial insight increases your chances at a successful implementation.
Data governance plays a crucial role at many larger companies, especially those dealing with significant regulatory and compliance requirements, as highlighted above. Still, when considering data privacy, they remain one of the main reasons companies make large investments in maintain a strong SecOps footprint. However, adopting ChatGPT and generative AI raises a whole new specter of potential problems related to the privacy of corporate data, including proprietary information and trade secrets.
For example, what if a customer service chatbot mistakenly shares secret data about a new product currently in development? The language model used to train the bot’s responses included information that needed to be filtered out before going live. This scenario highlights the importance of a rigorous training and QA process before any new chatbot goes live.
In addition to a chatbot sharing private corporate information, what if it recommends a product or service from one of your competitors. Once again, advanced AI-powered chatbots are still effectively random word generators. If data from competitors is included in the language model used to train the chatbot, what’s to stop it from sharing information with customers? It provides another example why robust model training – including focused prompt engineering – becomes critically important.
So it becomes obvious that a well-managed and disciplined process needs to happen if your company hopes to successfully adopt ChatGPT or a similar generative AI tool. It becomes especially critical to develop a set of best practices for model training to ensure no mistakes are discovered after the tool goes live. In short, don’t let a poorly implemented generative AI app ruin your company’s reputation among its customers, clients, and vendors.
Any well-considered model training process for a chatbot leveraging generative AI needs to use vector embeddings and a siloed approach for the data used in the language models.
Include any public-facing product and other company data in the first solo. This ensures the chatbot’s output is effectively limited to this textual information, filtering any trade secrets. Additionally, create another silo used as a database for negative text. The chatbot should never use this content in its responses.
The process of actually testing the chatbot or tool using these silos leverages a technique known as prompt engineering. It involves QA engineers and superusers adopting the role of various personas by querying the chatbot and verifying the quality of its responses. Obviously, these testers also make sure no private information or trade secrets are mentioned in responses. Only the information from the first silo is used.
Additionally, the QA team verifies that any responses exclude data from the second silo. This ensures the chatbot doesn’t recommend competitors’ products or uses any obscene or otherwise embarrassing language in its output. Using both silos and vector embeddings in concert with rigorous prompt engineering remains a key aspect of any ChatGPT adoption effort.
These examples illustrate that avoiding data privacy issues when implementing a product using ChatGPT or other forms of generative AI requires a focused software development process. Simply turning your programmers loose on the ChatGPT API without sufficient guardrails might result in project failure or even significant embarrassment and regulatory issues for your business.
Despite the widely-publicized nature of ChatGPT and the promise of generative AI, a measured approach works best, especially for those companies where regulatory compliance remains paramount. If your company lacks expertise in training machine learning models, simply partner with a digital agency with the right experience.
When searching for a software development shop with deep experience in ChatGPT, generative AI, and large language models, look no further than the team at NineTwoThree. We boast a track record of success in ChatGPT development, combining the technical chops, experience, and entrepreneurial spirit your businesses needs. Connect with us to discuss your compelling idea for generative AI.