Ensuring the security of data and controlling access has become a fundamental principle, especially related to Large Language Models (LLMs). Role-based security stands out as a top concern for enterprises trying to leverage the benefits of generative AI and LLMs.

The influence of LLMs has dramatically reshaped the landscape of consumer-oriented applications. However, when it comes to use cases related to businesses, these models pose substantial challenges. The process of refining an LLM using organization-specific data requires diligent caution to avoid compromising security – the tradeoff between risk and benefit is like balancing on a tightrope.

Striking the right balance is tricky. Training your LLM on universally accessible general information may diminish its value. On the other hand, if it’s trained on highly valuable data, access has to be restricted to a limited user pool (due to the often confidential nature of such data). Essentially, balancing utility and security emerges as a paramount consideration, often presenting a hurdle that seems impossible to overcome.

This makes role-based security an indispensable asset. Guaranteeing precise access control for specific roles during technology and data interactions isn’t merely important; it’s absolutely essential.

Recently, we introduced SynthesisAI, an innovative technology that approaches this challenge from a novel angle, effectively addressing role-based security concerns among various others. Our solution incorporates an additional layer known as the Wand DataModel, seamlessly integrating with an enterprise’s role-based access security framework. This empowers businesses to harness the complete potential of LLMs and Predictive AI without having to worry about unwanted data visibility and the ability to avoid compromises on security.