The significant increase in the value of AI technology companies along with that of AI chipmakers in recent weeks attests to the global community’s shared expectation for expansive AI utilisation in the future. Specifically, it’s the Large Language Models (LLMs) within the broader AI landscape that are drawing attention. LLMs are machine learning models designed to understand, generate, and improve human language. They are trained on vast amounts of text data, allowing them to predict or generate human-like text based on the inputs they receive.
LLMs are globally used in various solutions, with ChatGPT being the most dominant tool. Some organisations enable their employees to use ChatGPT to significantly boost productivity. Besides ChatGPT, LLMs are also commonly utilised in several other ways, such as embedded solutions in the form of chatbots used in customer service platforms, transcription and summarisation tools, or document and code writing assistants.
Generally, these solutions can be categorised into two types: open-source and vendor-provided. Open source solutions are based on LLMs whose code, and sometimes even the trained model, are made public, allowing researchers and developers to use and modify them. In contrast, vendor-provided solutions are those that a specific vendor develops, hosts, and maintains, making them readily accessible to users, typically through an API.
To mitigate potential risks associated with LLM solutions, there are three key risk mitigation strategies that organisations can employ:
- Upgrade to the enterprise edition: In the case of vendor solutions, organisations can use an enterprise version of the LLM tool, such as ChatGPT Plus, which provides robust security features essential for safeguarding sensitive and proprietary information.
- Deploy a secure gateway: Organisations can implement and deploy a secure gateway that examines information fed into the solution and potentially exiting the organisation, enhancing security provisions and ensuring data integrity.
- Invest in a private cloud: By hosting an instance of the LLM solution on a private cloud using an open-source version, organisations can ensure their data remains fully isolated from external access and separate from vendor-managed networks.
In addition to these risk mitigation measures, it’s important for organisations to assess and remediate the full spectrum of risks to which LLM solutions are exposed, such as data security, data privacy, and the accuracy and reliability of transcriptions. This can involve encrypting data, establishing access controls, employing data anonymisation and masking techniques, and conducting regular security audits.