Recently, Large Language Models (LLMs) have emerged as one of the most transformative technologies of the 21st century. From healthcare to cybersecurity, LLMs have already had a significant impact on various industries. These AI-powered models are capable of processing vast amounts of text data, including threat intelligence reports. In the realm of Cyber Threat Intelligence (CTI, TI), LLMs are transforming the field by making it faster and more cost-effective.

An LLM is a type of AI model that uses deep learning techniques and trained on large amounts of text data. The usage of a neural network with billions of parameters allows them to understand the complex relationships between words and phrases in text data. This enables LLMs to learn from large datasets and become better at understanding and processing natural language. Once trained on TI report data, LLMs can automatically classify such reports based on their content, extract key facts such as indicators of compromise (IoCs), TTPs, SIGMA, YARA and other relevant data, and even prioritize the most critical threats.

While LLMs are a powerful tool for threat intelligence analysis, it is important to keep in mind their potential limitations. For example, LLMs may struggle with slang, sarcasm, jargon, or other types of specific language that humans can easily understand. Moreover, LLMs are only as good as the datasets they are trained on. Therefore, it is crucial to ensure that the data used to train LLMs is accurate and representative.

One of the key challenges in threat intelligence analysis is the enormous volume of reports that need to be collected, preprocessed, and analyzed. Thousands of TI reports are issued every year. Traditionally, the CTI process involved manual reading and categorizing huge number of various reports, which was time-consuming and prone to errors.

While AI/ML algorithms had been used before, LLMs have the potential to revolutionize the way cybersecurity professionals analyze and respond to threats. They can automatically analyze and categorize reports, reducing the time and resources required for threat intelligence analysis. Also, by analyzing the content of TI reports, LLMs can determine the severity of the threats and prioritize them accordingly. Such automation significantly reduces the effort and resources required for threat intelligence analysis, freeing up human analysts to focus on more interesting work.

Another big challenge in CTI has been the need for diversity of analysts with different skill sets and mindsets to analyze the data at both deep technical, operational, and strategic levels. On the one hand, technical analysts are primarily focused on understanding the technical details, such as the specifics of threats, reverse engineering of malware, or cryptography usage in malicious programs. Strategic analysts, on the other hand, are interested in the broader context of threats, including the motives and tactics of threat actors, where the trends are going.

LLMs are uniquely positioned to bridge this gap. They can analyze technical reports with a similar level of accuracy as a technical analyst, while also providing the broader context and strategic insights typically associated with a strategic analyst. This means that LLMs can effectively operate in both worlds, providing valuable insights into the motives and behavior of threat actors. For instance, LLMs can identify changes in a threat actor's TTPs or detect emerging threats based on changes in their behavior, and at the same time provide the exact IoCs and TTPs that are required to detect such activity from technical perspective.

Obviously, LLMs are improving the field of CTI by enabling faster and more efficient analysis of threat intelligence reports. They can operate in the technical, operational, and strategic TI, reducing the need for multiple analysts with different skillsets. As LLMs evolve so quickly, they will undoubtedly become an increasingly important tool in the fight against cyber threats.