Large language models (LLMs) are advanced machine learning models trained on extensive text datasets to understand and generate human-like language.
Large language models (LLMs) are a powerful class of artificial intelligence (AI) reshaping both the threat landscape and the tools used to defend it. From automating phishing attacks to detecting them, LLMs are now part of both the problem and the solution.
Read on to explore what security leaders need to know about LLMs—their risks, use cases, and expanding role in enterprise defense.
Large language models (LLMs) are powerful deep learning algorithms that use neural networks and the transformer architecture to understand and generate text close to human language.
In other words, LLMs like OpenAI’s GPT or Google’s BERT are trained on massive amounts of text using deep learning, so they can predict the next word in a sentence.
This gives LLMs the ability to:
Generate Text: Generate natural language for phishing emails, malicious scripts, or to automate security reports and documentation.
Summarize Content: Condense threat reports, incident logs, and alerts for faster analyst review.
Translate Content: Converts threat intel, attacker communications, or logs across languages with high accuracy.
Analyze Sentiment: Detects tone, intent, or emotional cues in emails and chats to flag social engineering or insider risks.
Answer Questions: Powers security assistants and chatbots to surface documentation, triage alerts, or guide response actions.
LLMs have become foundational in advancing conversational AI, virtual assistants, content creation, and cybersecurity, thanks to their ability to process and emulate human communication effectively.
LLMs use a transformer architecture, a neural network that processes text by understanding the context and relationships between words.
Here’s a breakdown of how they work:
During pre-training, an LLM learns from vast amounts of text, such as books, articles, and websites. This helps the model understand language rules, grammar, and how words relate to each other. The key steps include breaking text into smaller pieces (tokenization) and using a self-attention mechanism to figure out which words are most important in a sentence.
Once trained, the model can generate or analyze text in real time. It can also predict the next word based on the context, allowing it to create clear sentences, imitate writing styles, and offer helpful information.
LLMs come in various forms, typically categorized by how they’re trained and what they’re optimized to do.
Common types include:
General-Purpose Models: Versatile models like OpenAI’s GPT and Google’s BERT, trained on broad datasets, can perform a wide range of tasks with minimal customization.
Domain-Specific Models: Models fine-tuned on specialized datasets for targeted use cases. Examples include BioBERT for the biomedical industry and FinBERT for the financial industry.
Instruction-Tuned Models: Models trained to follow prompts or commands more accurately. Examples include FLAN-T5 and OpenAI’s GPT-4 with instruction tuning.
Conversational Models: Models fine-tuned specifically for dialogue, like ChatGPT or Bard, that are optimized to understand context and maintain natural interactions.
Most modern LLMs combine several of these capabilities, blending general-purpose architecture with fine-tuning to serve specific tasks like conversation, summarization, or coding.
LLMs excel in understanding nuanced language, making them uniquely effective in detecting email-based threats that rely on subtlety and context. These models enhance the ability to:
Identify and block advanced phishing attempts.
Detect anomalies in email behavior that traditional systems miss.
Adapt rapidly to new attack methods using real-time learning.
Abnormal uses large language models (LLMs) like BERT and GPT to transform email threat detection. These models analyze email content in context, identifying sophisticated threats like business email compromise (BEC) and phishing that traditional tools often miss. Here’s how:
LLMs enable advancements in natural language processing (NLP) across industries. Their ability to understand, generate, and analyze text has far-reaching applications, from improving business communication to enhancing cybersecurity. As LLM technology continues to evolve, its impact will undoubtedly expand, driving innovation in countless domains.
LLMs offer powerful AI capabilities in understanding and generating language but require careful governance, significant resources, quality data, and technical expertise to ensure ethical, accurate, and effective deployment.
Here are some ethical and practical challenges that organizations must navigate carefully before and during LLM deployment:
Power and Potential: LLMs are powerful, but their complexity makes them difficult to control, audit, and deploy safely at scale.
Bias and Fairness: Training data can reflect social, cultural, or institutional biases, leading to outputs that reinforce unfair or harmful patterns.
Factual Accuracy: LLMs often generate convincing but incorrect information, making them unreliable without human oversight.
Data Privacy: Without strict safeguards, LLMs can expose or memorize sensitive data, posing serious risks in regulated industries.
Resource Demands: Training and running LLMs require significant compute power, energy, and financial investment, limiting access to well-funded organizations.
Data Scale Challenges: Effective training depends on massive, high-quality datasets—something many teams can’t access, especially in niche domains.
Technical Expertise: Building, fine-tuning, and safely deploying LLMs requires deep AI and infrastructure knowledge, posing a barrier for most teams.
As LLM technology evolves, its impact will undoubtedly expand, driving innovation in countless domains.
Ready to see how Abnormal’s LLM-powered platform can protect your organization? Book a personalized demo today and experience next-generation email security in action.
