Today, Generative AI has become essential with ChatGPT, Midjourney, and even Google Bard. It is everywhere, including businesses. However, alternatives exist. Today, Generative AI has become essential with ChatGPT, Midjourney, and even Google Bard. It is found everywhere, including in companies that see it as a way to increase productivity.
However, their leaders would be wise to exercise caution if they value the confidentiality of their data. Many AIs, ChatGPT in the lead, do not guarantee it. Fortunately, there are secure and equally effective alternatives to help them accelerate their projects.
Proven Cases Of Sensitive Data Leaks
Open AI unveiled ChatGPT just a year ago, and since then, the progress has been extraordinary. The conversational agent can generate text, answer customer questions via chatbots, edit code, and solve complex problems—an exceptional potential that appeals to companies and their employees. However, today, authorizing ChatGPT in the world of work means introducing the wolf into the sheepfold.
If this tool appears attractive, we know little about its use of our data or how it was trained. In its privacy policy, Open AI indicates that it shares the content of conversations with various entities, including suppliers, legal actors, and even AI specialists. Recently, Samsung Electronics employees unintentionally disclosed sensitive information: one notably copied an entire source code to unlock a bug on a ChatGPT application dedicated to his profession.
This is an error with severe consequences in the strategic field of semiconductors. This pitfall is not ChatGPT’s prerogative. Copilot, Github’s tool for developers, has also come under fire on copyright. The AI was accused of training with lines of code which, although Open Source, were subject to specific conditions of use.
Open Source: A Way To Reconcile AI And Data Confidentiality
Under these conditions, it is difficult for companies to let their employees freely use ChatGPT or other similar tools. However, it would be a shame for them to deprive themselves of their tremendous potential. Compared to a ChatGPT, their significant advantage is that they run them directly on the company’s servers, thus guaranteeing data confidentiality.
The information disseminated there never leaves the internal context. Another advantage is that it is also possible to improve them by training them using company data. Nvidia, for example, designed its own AI by implementing LLaMA 2 to help its engineers respond more quickly to their customers.
PaaS, A Promising Alternative
Another solution for companies that cannot deploy such models internally lies in PaaS, Platform as a Service. These are now deployed in a growing number of Data Centers. They provide companies with a quickly available execution environment while giving them control over the applications they can install there—an effective way to use AI like LLaMA 2 or Mistral AI, with sufficient confidentiality guarantees.
Although these solutions are costly today, their prices continue to fall in a highly competitive context. In a few months, they should become much more accessible. Whatever the tools used, pedagogy nevertheless remains essential. We are at the beginning of Generative AI, and many subjects remain to be explored.
Without guaranteeing perfect data protection, employees and developers must be extremely careful not to disclose proprietary codes or innovation subjects that could jeopardize their company. Ultimately, regulations will undoubtedly provide an additional framework in this area. For now, Generative AI remains an immense virgin territory, carrying great promises but risks that must not be sacrificed on the altar of productivity.
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