Improving cybersecurity with LLMs: How OpenAI says to do it

If you're wondering how to implement large language models (LLMs) to improve cybersecurity in your organization, here's what OpenAI's Head of Security, Matthew Knight, thinks would be a good place to start for your organization.

During our coverage of RSA Conference '24, we jumped into a seminar where Matthew was sharing practical examples on how to make life easier for cybersecurity analysts and engineers. Some of these will be familiar if you've read Laurentiu Raducu’s article on how to do this last week (great to see our authors are ahead of the curve!).

How can you apply LLMs to improving your organization's cybersecurity
 

1. Showcasing LLM’s ability for tl;dr’s, multilingual data analysis, threat reports

It’s well known that security professionals have an abundance of information to consume and literally not enough time in the day to do it. Matthew showed a rather neat example of using GPT, where he took a Russian news article on how the government handles personal data, and used the LLM to create a counterintelligence brief for a company that suggested proposed changes to personal data processing in Russia. 
 

Quite a time saver if you don’t want to learn Russian. 

While this isn’t exactly new in terms of what LLMs can do, it’s worth pointing out that this can be used by cybersecurity professionals to make their life easier. Matthew mentioned that in their experience, if you have a particular intelligence analyst template you like to use, you can give it to the LLM and it’ll do a decent job of matching it.

Matthew also showcased how LLMs could be used to summarize otherwise tediously long technical documents - something that every security engineer would appreciate saving time on.