Can Large Language Models Boost Your Security Posture?


The threat landscape is expanding, and regulatory requirements are multiplying. For the enterprise, the challenges just to keep up are only mounting.


In addition, there’s the cybersecurity skills gap. According to the (ISC)2 2022 Cybersecurity Workforce Study, the global cybersecurity workforce gap has increased by 26.2%, which means 3.4 million more workers are needed to help protect data and prevent threats.


Leveraging AI-based tools is unquestionably necessary for modern organizations. But how far can tools like ChatGPT take us with regard to boosting cybersecurity and addressing the skills gap?


ChatGPT is dominating the tech news cycle. Some can’t get enough, but others are sick of hearing about it. But what about AI in cybersecurity? Is it any different? 


While ChatGPT certainly has numerous use cases, there are some notable shortcomings that enterprises must understand before they dive head-first.


Transformers: More Than the Toys and Movies


First, a bit of background on large language models, which have undergone a remarkable transformation over the last few years.


Early models relied on basic statistical methods to generate text based on the probability of word sequences. As machine learning improved, more advanced models like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks emerged — offering better contextual understanding and text-generation functions.


But the turning point of natural language processing (NLP) was the introduction of transformer architectures in 2017. That’s where OpenAI’s popular GPT comes i ..

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