Building an LLM-Powered Cybersecurity AI Assistant

The ever-shifting landscape of cyber threats demands constant adaptation and expertise from security professionals. This is where Large Language Models (LLMs) are making a transformative impact. By enabling the development of specialized AI assistants, LLMs empower professionals to work smarter and faster. Here at Acalvio Technologies, we have harnessed the power of LLMs to create an AI Assistant that transforms the way security professionals leverage standardized cybersecurity frameworks. Imagine an assistant that sifts through data to identify threats, and even helps develop effective response strategies – all powered by the vast knowledge and contextual understanding of LLMs.

Of course, challenges remain. The computational power required and the need for specialized training present hurdles. However, the potential benefits are undeniable. Leveraging the wealth of cybersecurity defensive knowledge sources, the Acalvio AI Assistant provides comprehensive, real-time, up-to-date answers, thereby enhancing user efficiency and effectiveness.

This blog post delves into the process of building a LLM based AI assistant. We’ll discuss the key components involved, including LLMs, datasets, RAG technology, vector databases, and how we evaluate the final product.

Development Lifecycle of the AI Assistant

Building a cybersecurity AI assistant involves several key stages to ensure its effectiveness. Let’s explore each stage in detail

Data Collection and Context Enhancement

The cybersecurity AI Assistant relies on careful data gathering and organization from MITRE STIX. This involves steps like collecting, preprocessing, converting to .md format, evaluating, and storing data in vector database, ensuring accuracy amid changing cyber threats.

LLM Selection

An LLM is a deep learning algorithm that can perform a variety of natural language processing (NLP) tasks. LLMs use transformer models and are trained using massive datasets — hence, large. This enables them to recognize, translate, predict, or generate text or other content.

Choosing the right LLM is crucial for the effectiveness of the AI Assistant. Factors like context length, model size, domain relevance, customization options, licensing, and ethical considerations are important.

Additionally, LLMs Assessment is crucial for refining the AI Assistant’s capabilities. By carefully reviewing and benchmarking against test cases, we evaluate factors like answer accuracy, response time, and sensitivity to input prompts. This iterative process helps us adjust the AI Assistant for optimal performance while maintaining ethical use and professional standards. Through careful evaluation of different LLMs, including falcon-7b-instruct, Falcon-7b, llama2-7b, llama2-7b-chat-hf, and mistral-7b-instruct, we have built an AI Assistant that meets the criteria for addressing cybersecurity queries.

Retrieval Augmenter Generation (RAG)

To develop an AI assistant capable of responding to queries related to cyber attack Tactics, Techniques, and associated tools, it is essential to first gather and organize relevant information for the LLMs to use. This process of optimizing the output of an LLM, so it references an authoritative knowledge base outside of its training data sources before generating a response is called Retrieval Augmented Generation.

LLMs, equipped with billions of parameters and trained on extensive data, produce original output for tasks such as answering questions. RAG builds upon the robust capabilities of LLMs, extending them to specific domains or an organization’s internal knowledge base without requiring model retraining. This methodical approach ensures streamlined information retrieval.

Vector Database and Semantic Search

Vector databases serve as the backbone of our AI Assistant, enabling swift data retrieval and immediate access to relevant information. These databases are engineered for speed and scalability, making them indispensable for managing vast data volumes. Considering various factors such as hosting options, features, and performance, we have selected a suitable solution for its simplicity and open-source nature.

Semantic search plays a crucial role within the assistant system by enabling the retrieval of snippets from documents that are relevant to user queries. This process involves breaking down documents into smaller, more manageable snippets, which are then translated into a statistical format known as vector space. Essentially, this process transforms textual data into a numerical representation, allowing for it to be stored within a database.

Results and Evaluation

Our ongoing efforts to refine the AI Assistant have yielded promising results. Through improvements in precision and answer accuracy, we’ve enhanced the AI Assistant’s effectiveness in addressing cybersecurity queries. As we look to the future, we are excited about the possibilities of expanding our AI Assistant’s capabilities further, ensuring its relevance and accuracy in tackling emerging cybersecurity threats.

In conclusion, the development of the cybersecurity AI Assistant highlights the significance of collaboration and innovation. By defining goals, managing data efficiently, and focusing on user needs, we’ve built a valuable tool in combating cyber threats. As we further develop and improve the AI Assistant, we are excited to share insights and engage in discussions about AI’s role in cybersecurity and beyond.

Authors info

Shivaraj Mulimani
Shivaraj is Data Scientist at Acalvio, specializes in cybersecurity, demonstrating expertise in Machine learning, NLP, and R&D with over 6 years of work experience.

Arunkumar M P
Arun is a passionate data scientist with an M.Sc in Theoretical Computer Science. He has been actively contributing to his role within Acalvio’s Data Science team for 2 years.

Nirmesh Neema
Nirmesh is Senior Data Scientist at Acalvio. He has successfully tackled numerous real-world cybersecurity challenges utilizing cutting-edge AI/ML techniques with 10+ years of work experience.

Dr Satnam Singh
Dr Satnam Singh is leading security data science development at Acalvio. He has more than 20 years of work experience in successfully building data products to production in multiple domains. He has 25+ patents and 30+ journal and conference publications