In 2025, OpenAI reported roughly 700 million weekly active users for ChatGPT alone. That figure that doesn't even account for the hundreds of millions more using competing tools like Google Gemini, Microsoft Copilot, and Anthropic's Claude. All of them are built on a class of AI called large language models or LLM.
A large language model is a deep learning system trained on massive volumes of text to understand and generate natural language. But the implications are anything but simple. In this guide, we cover what LLMs are, how they work, their most common use cases, and why they matter for artificial intelligence at scale.
What Is an LLM?
A large language model (LLM) is a type of artificial intelligence built on neural networks and trained on billions of words of text data. A LLM is trained to understand, predict, and generate human language.
LLM AI systems fall under the broader category of generative AI. They don't just classify or retrieve information, they produce new content: answers, summaries, code, translations, and more.
LLMs are foundation models, meaning they are trained once on general-purpose data at massive scale and can then be adapted and fine-tuned for specific tasks without starting from scratch.
What distinguishes LLMs from previous AI approaches is the scale of their training data and the sophistication of their architecture. Where earlier systems required hand-crafted rules or task-specific training datasets, LLMs learn statistical patterns across human language broadly. This helps the LLM generalize across a wide range of tasks with little or no additional training.
LLMs have a variety of uses in cyber security. For example, Rubrik Security Cloud integrates LLM-based intelligence to help security teams interpret unstructured data, surface anomalies, and accelerate incident response across complex enterprise environments.
How Do LLMs Work?
LLMs are built on a neural network architecture called the transformer, introduced by Google researchers in 2017. Transformers process text by breaking it into tokens—small chunks of characters or words—and learning the statistical relationships between those tokens across enormous training datasets.
The training process works in two broad phases:
Pre-training: The model processes billions of documents (books, websites, code, scientific papers, etc.) learning to predict the next token in a sequence. This is computationally intensive and requires specialized hardware running for weeks or months.
Fine-tuning: Once pre-trained, the model is refined on a narrower dataset aligned to a specific task or set of behaviors. This is how a general-purpose LLM becomes a customer support assistant, a coding tool, or a security analyst aid.
Phase | Purpose | Data Used | Duration |
|---|---|---|---|
Pre-training | Learn general language patterns | Billions of documents | Weeks to months |
Fine-tuning | Adapt to specific tasks or behaviors | Thousands to millions of examples | Hours to days |
According to research from Stanford's Human-Centered AI group, the largest LLMs now contain hundreds of billions of parameters—the numerical weights that encode what the model has learned. Deep learning techniques allow these parameters to capture nuanced patterns in language that earlier machine learning methods could not.
Common Use Cases for LLMs
LLMs have moved far beyond research labs. Today they power a wide range of tools across industries:
Text generation: Writing assistants, summarizers, and content drafting tools (ChatGPT, Claude, Gemini)
Code completion: Developer productivity tools that suggest and complete code in real time (GitHub Copilot, Amazon CodeWhisperer)
Customer support: Chatbots and virtual agents that handle tier-1 support queries without human escalation
Language translation and grammar correction: Real-time translation and editing across dozens of languages
Data classification and tagging: Automated labeling of unstructured data at enterprise scale
In cybersecurity specifically, LLMs are increasingly embedded in threat monitoring platforms to interpret log data, classify risk signals in unstructured documents, and generate plain-language incident summaries for security operations teams—reducing the time analysts spend on manual triage.
Why LLMs Matter in the AI Landscape
LLMs have fundamentally changed what artificial intelligence can do with natural language. Prior to their emergence, natural language processing was largely rule-based or statistical—capable of classifying sentiment or extracting named entities, but unable to generate coherent long-form responses or reason across diverse topics.
Today, LLMs are accelerating AI adoption across enterprise, SaaS, and security ecosystems. Their ability to interpret unstructured data—emails, contracts, logs, support tickets—at scale gives organizations a new lever for automation and intelligence that was previously inaccessible.
In security contexts, LLMs enable zero-trust frameworks to extend into unstructured data: interpreting the risk signals embedded in documents and communications that traditional rule-based systems cannot parse. Rubrik's unstructured data protection capabilities use AI-driven analysis to discover, classify, and protect sensitive data regardless of format—a critical capability as enterprise data volumes grow and attack surfaces expand.
According to McKinsey's 2024 State of AI report, generative AI adoption doubled year-over-year in enterprise environments, with large language models cited as the primary driver of new automation use cases.
Large language models are no longer a research curiosity—they are the infrastructure behind a new generation of AI-powered tools reshaping how enterprises operate, communicate, and protect their data. Understanding what an LLM is, how it works, and where it applies is foundational knowledge for anyone navigating the AI landscape in 2026 and beyond. Contact Rubrik sales and learn how AI and cybersecurity intersect—and what it means for your organization.