Managing the Security and Privacy Issues with Large Language Models
Everyone is buzzing about ChatGPT, Bard, and generative AI. But, inevitably, the reality check follows the hype. While business and IT leaders are excited about the disruptive potential of technology in areas such as customer service and software development, they are also becoming more aware of some potential downsides and risks to be aware of.
In short, for organisations to realise the full potential of large language models (LLMs), they need to be able to deal with the hidden risks that could otherwise undermine the technology’s business value.
What exactly are LLMs?
LLMs power ChatGPT and other generative AI tools. They process massive amounts of text data using artificial neural networks. The model can interact with users in natural language after learning the patterns between words and how they are used in context. In fact, one of the main reasons for ChatGPT’s extraordinary success is its ability to tell jokes, compose poems, and communicate in a way that is difficult to distinguish from that of a real human.
The LLM-powered generative AI models used in chatbots like ChatGPT function like supercharged search engines, answering questions and finishing tasks with human-like language using the data they were trained on. LLM-based generative AI, whether publicly available or proprietary models used internally within an organisation, can expose businesses to security and privacy risks. Here are the three of the most prevalent LLM risks:
Excessive sharing of sensitive information
LLM-based chatbots are not adept at maintaining secrets, or even forgetting them. This implies that any data you enter could be incorporated into the model and shared with others, or at the very least, used to train LLM models in the future. When Samsung employees used ChatGPT for work-related purposes and disclosed sensitive information, they discovered this to their disadvantage. The code and meeting recordings that they input into the tool might potentially be in the public domain, or at the very least saved for later use, as the National Cyber Security Centre of the United Kingdom recently noted. We looked more closely at how businesses can use LLMs without compromising their data earlier this year.
LLMs are trained on huge quantities of data. However, that data is frequently scraped from the web without the explicit authorization of the content owner. If you continue to use it, you may encounter copyright issues. However, finding the original source of specific training data can be difficult, making it difficult to mitigate these issues.
More and more developers are using ChatGPT and related tools to shorten their time to market. Theoretically, it can be useful by rapidly and effectively generating snippets of code and even full software programmes. Security experts warn that it may also lead to vulnerabilities. If the developer lacks sufficient domain knowledge to identify which bugs to look for, this is especially concerning. If flawed code is then released into production, it might severely damage the company’s reputation and cost money and effort to fix.
Data encryption and anonymization: To keep data safe from prying eyes, encrypt it before sharing it with LLMs, and/or consider anonymization techniques to safeguard the privacy of individuals who could be recognised in the datasets. Data sanitization, which removes sensitive information from training data before it is entered into the model, can achieve the same result.
Enhanced access controls: Strong passwords, multi-factor authentication (MFA), and least privilege policies will help ensure that only authorised individuals have access to the generative AI model and back-end systems.
Regular security audits: This can help in the identification of vulnerabilities in your IT systems that may have an impact on the LLM and generative AI models on which they are based.
Fortunately, there’s no need to start from scratch. These are mainly tried-and-true security best practises. For the AI world, they might need to be updated or adjusted, but most security teams should be able to understand the underlying reasoning.