With the increasing popularity and capabilities of chatbots powered by large language models (LLMs), companies worldwide are exploring their potential to streamline business workflows and processes. However, before jumping on the bandwagon and integrating LLM-powered chatbots into their operations, businesses must be aware of the potential risks and take necessary precautions to safeguard their data.
One major concern is the safety of company data shared with LLMs. These algorithms are trained on large amounts of text available online, which means that companies may inadvertently hand over their business and customer data every time they make a request to the chatbot. Even if LLMs do not automatically add information from queries to their model for others to access, the queries themselves could be visible to the organization providing the LLM. Moreover, once stored, queries could be used to develop the LLM service or model at some point in the future, potentially putting sensitive data at risk.
To address these concerns, some LLM providers, such as Open AI, have provided the ability to turn off chat history. However, in the event of a hack or data leak, queries stored online could still be at risk of being compromised or made publicly accessible.
Another issue to consider is the potential flaws in LLM security. While the technology has generally been secure thus far, a few incidents have occurred. For instance, OpenAI’s ChatGPT suffered a leak of some users’ chat history and payment details due to a bug in an open source library. Additionally, security researchers have demonstrated how Microsoft’s LLM Bing Chat could be exploited to trick users into giving up their personal data or clicking on a phishing link.
Several companies have already experienced LLM-related incidents in their own organizations, most notably in Samsung Electronics. To avoid similar issues, businesses must carefully investigate how these tools and their operators access, store, and share their data, and develop a formal policy covering how their companies will use generative AI tools. This policy should also define the circumstances under which employees can use the tools and make staff aware of limitations, such as never putting sensitive company or customer information into a chatbot conversation.
When implementing LLM chatbots, employees should use them as advisors who need to be checked, verify outputs for accuracy, and consider possible copyright issues. In order to safeguard data privacy, businesses should put in place access controls, teach employees to avoid inputting sensitive information, use security software with multiple layers of protection, and protect data centers through security measures similar to software supply chains.
In conclusion, while LLM-powered chatbots offer many benefits for businesses, it is crucial to understand the potential risks and take necessary precautions to avoid putting sensitive data at risk. Companies must not only investigate the security of LLMs themselves but also ensure that their own policies and practices align with current data privacy regulations. Only then can they safely embrace the potential of LLMs to boost efficiency and productivity.