Solving Enterprise Data Privacy and Security Concerns with Generative AI
As we near the 6 month mark of generative AI dominating the headlines, the conversation has quickly shifted from amazement to more practical considerations and risks. In response to leaks of confidential and private data, one of the biggest topics of discussion related to generative AI has quickly become data privacy and security, particularly for enterprises and government organizations.
One of the biggest security and privacy risks is caused by what is known as a ‘shared model backbone’, referring to when a generative AI tool uses a single model across all of its users and customers. The implication of this is that any data that is used to interact with a generative AI tool, such as ChatGPT, becomes part of the model, improving it over time. However, it also means that this data can be accessed by other users. Unsurprisingly, for enterprises and government organizations this poses serious security and privacy concerns
In one high profile example, employees at Samsung inadvertently leaked confidential information by sharing meeting minutes and source code in a ChatGPT prompt.
In another example, it was revealed that a bug resulted in leaked sensitive information about ChatGPT user data.
Overcoming these issues requires a fundamentally different approach to enable generative AI for enterprises and government organizations. Generative AI must be deployed within a customer’s firewall, and provide the organization with its own ‘dedicated model backbone’. This means that the organization has its own unique generative AI model that is not shared with any other customers, and can use its own data to adapt and interact with the model without risk of that information being leaked. It also enables these organizations to retain ownership of the models built in this way.
Click here to learn more about how SambaNova Suite delivers generative AI optimized for the enterprise.