In the intricate dance of digital security, where every byte and bit could harbor the key to understanding a potential vulnerability or breach, the role of security logs is undeniable. These logs, dense forests of technical data, serve as the cryptic scrolls of the digital realm, holding within them the tales of every event, transaction, and anomaly. Yet, for those tasked with deciphering these tales, the challenge is not just in the reading, but in the understanding and interpreting of this complex data. Herein lies the revolutionary potential of large language models (LLMs) in transforming the landscape of digital security. By harnessing the power of advanced artificial intelligence, these models oFer a beacon of clarity, translating the arcane scripts of security logs into the fluent prose of natural language. This innovation not only democratizes the understanding of security data, making it accessible to a broader range of professionals and stakeholders, but also obviates the need for security products to develop intricate parsers to interpret data from other products. It's a leap towards a more integrated, comprehensible, and eFicient framework for digital security, where the focus shifts from the laborious task of data interpretation to the critical analysis and decision-making that safeguard our digital frontiers.
If the previous paragraph sparked your interest, allow me to introduce myself. I'm John Peterson, often referred to as "JP," and I'm one of the founders and inventors at PRE Security. At the inception of PRE Security, we were driven by a bold vision: to create the world's first cybersecurity product capable of predicting cyberattacks before they occur. Our goal was to design a solution that seamlessly integrates with the existing tools within organizations. We understood the challenges new products often face in the market, particularly when they introduce significant deployment or budget constraints. Our mission was to develop a product that could serve as a neutral hub for data ("Data Switzerland"), embracing data diversity and eFortlessly integrating into any organization's cybersecurity framework. By achieving this, we aimed to eliminate the dilemma CISOs and SOC operators face when considering replacing recent investments for the latest innovations, avoiding those uncomfortable conversations with CFOs about discarding recently acquired tools for new ones.
What did we achieve? We developed an innovative technology called Log2NLP. This technology transforms any data from cybersecurity tools into human language, the most universal form of communication. Understanding cybersecurity logs is often challenging due to their complex nature, especially when dealing with logs from various sources. As someone who has been writing software for over 45 years, I've seen firsthand how logs can become confusing with the use of abbreviations, acronyms, and shorthand terms like src_ip and dst_ip. Typically, the clarity of logs isn't a priority in development; the focus is usually on the product's functionality or user interface. This approach leads to logs that are diFicult for people to understand, which in turn makes it hard for diFerent products to interpret the data.
Simplifying log data into a universal format that both humans and cybersecurity tools can easily understand is an obvious goal. The challenge has been in finding an eFective way to do this, until now. Thanks to advancements in large language models (LLMs) and artificial intelligence, we can now convert complex log data into understandable human language. LLMs are well-versed in human language, including acronyms, abbreviations, and technical terms, because they have been trained on vast datasets. This training makes LLMs not just capable of translating between human languages, like Spanish to English, but also adept at translating technical log data into natural language. It’s kind of like having a digital linguist at your disposal!
Our innovative Log2NLP (log to natural language processed log) technology at PRE Security translates log data into understandable text by processing data from any cybersecurity tool with our specialized large language model (LLM). This LLM is expertly trained on extensive datasets (including various tool logs), enabling it to interpret log data more accurately and quickly than any human. For example, it knows that "src_ip" refers to the source IP address, "dst_port" indicates the destination port, and "virus" can also mean malware. This level of understanding and speed allows for highly eFicient log analysis. Simply put, our LLM, like most LLMs perform “inference”. It can infer what something means and output it in plain old natural language.
Another advantage of this method is its ability to demystify complex logs for users. By converting logs into natural language, we make them easily readable and understandable. Here's an example of how a cryptic log is transformed into a clear, natural language log.
NATURAL LANGUAGE ALERT
The tool, Fortinet, with event id 1e68b3f, detected an event named port scan. This event was detected on August 8, 2023 at 06:54:33. The source IP address was 132.172.102.37 and the source tcp port was 40203. The destination IP address was 221.132.245.212 and the destination tcp port was 28734. The protocol used was TCP. The source IP address is located in United States and the destination IP address is located in Indonesia."
After converting logs into natural language, we apply a document embedding model to vectorize them and store the results in a vector database. This process enables us to deploy sophisticated machine learning algorithms to make context-aware predictions, like forecasting potential cyber attacks. Additionally, it allows for interactive queries with the dataset using our SOCGPTTM conversational bot, similar to ChatGPT.
To encapsulate the transformative approach PRE Security is championing, consider the traditional landscape where the integration of diverse cybersecurity tools hinges on the labor-intensive development of parsers. These parsers, essentially software translators, are tasked with converting the unique data language of one tool into the digestible format of another, a process both time-consuming and increasingly seen as archaic. Fast forward to the innovative horizon we're shaping: our platform is ingeniously designed to comprehend and operate on natural language. This leap forward eliminates the need for parsers altogether, allowing for seamless and immediate integration of data from any cybersecurity tool. Imagine the paradigm shift this represents—a world where the cumbersome process of developing, testing, and deploying hundreds of parsers for a myriad of tools becomes a relic of the past. By embracing natural language processing at the core of our platform, we not only streamline the integration process but also pave the way for a more agile, eFicient, and future-proof cybersecurity ecosystem. This is the vision and promise of PRE Security, where the intricacies of cybersecurity data are navigated with the ease and intuitiveness of human conversation, marking a significant milestone in our journey towards a more interconnected and accessible digital world.
In conclusion, the groundbreaking shift towards natural language processing in cybersecurity log analysis, as demonstrated by our Log2NLP technology, not only simplifies the integration of diverse security tools but also revolutionizes how we interact with and understand complex data. By transforming cryptic logs into clear, comprehensible text and employing advanced machine learning for predictive analysis, we are setting a new standard in cybersecurity. This approach not only enhances the eFiciency of threat detection and response but also democratizes access to critical security insights, making it possible for a wider range of professionals to participate in safeguarding our digital world. The future of cybersecurity is here, and it speaks our language.
John Peterson,
Co-CEO / Inventor
Comments