Before a system can create useful language (an output), it needs to understand what is being said. In large language models (LLMs), natural language understanding (NLU) allows systems to interpret what is meant, the intent, and the context from human language. This is made possible through a collection of capabilities exhibited by NLUs.
An Artificial Intelligence in Medicine systematic review on NLP notes, unstructured text, “cannot be directly used in clinical tasks” until it is interpreted and transformed through language understanding techniques.
At a granular level, NLU's capabilities support subtasks performed within generative AI. It breaks down text into manageable pieces and analyzes sentence structure, identifying things like word role and grammatical relationships so that models can interpret meaning even when phrasing is unclear. The same review found that over 90% of studies (74 out of 79) relied on NLP and machine learning techniques specifically to extract meaningful information from free-text narratives.
NLU also focuses on meaning, a feature that helps systems understand that the same word can mean a variety of things depending on its context. This, combined with the ability to maintain coherence, links ideas from one sentence to the next. Generative AI models, especially in email security, can therefore produce consistent and logical responses that are easy to follow rather than being fragmented.
NLUs are part of natural language processing (NLP) with a narrower and more specific focus. NLPs cover the full range of techniques used to work with human language, like processing text or speech. In this sense, NLPs are the broader discipline, and NLUs are responsible for tasks like semantic interpretation and contextual reasoning.
As one narrative review on NLP in healthcare ‘The Growing Impact of Natural Language Processing in Healthcare and Public Health’ explains, “NLP is a subfield of computational linguistics, focused on Artificial Intelligence (AI) models that interpret and generate human language.”
NLUs make sense of unstructured text. For example, clinical notes are often written in free-form language that includes shorthand and incomplete sentences. NLU techniques allow systems to identify entities like diseases or treatments and determine how they relate to one another.
From a structural perspective, NLP is a layered system. It starts with basic language elements like word forms and grammar, then moves upward into meaning, intent, and discourse. NLU operates at these higher levels, handling tasks such as recognizing named entities, identifying relationships between words, and interpreting sentiment or tone.
While NLP covers everything involved in working with human language, both understanding it and generating it, NLU is specifically concerned with figuring out what a piece of text actually means. According to a Multimedia Tools and Applications review, “NLP can be classified into two parts, i.e., Natural Language Understanding and Natural Language Generation, which involves the task to understand and generate the text.” NLP can be broken down into two main components. NLU, which interprets language, and natural language generation (NLG), which turns that understanding into coherent, readable text.
NLU handles the analytical side of language. It works across multiple layers of linguistics, from basic structure and grammar to meaning, context, and intent. Through this process, systems can identify concepts, entities, keywords, and even emotional cues within text. These capabilities make it possible to perform tasks like semantic interpretation, recognizing what a user is trying to achieve, resolving references within a conversation, and drawing conclusions based on context.
Generative AI systems depend heavily on natural language understanding to function effectively. Before these models can generate a response, they must first make sense of the input. This foundational understanding is what allows a model to respond in a way that feels relevant and accurate. To develop this capability, LLMs are pretrained on large and diverse text collections, which helps them learn how language is used across different domains.
As recent research on the use of generative AI in healthcare ‘Generative AI/LLMs for Plain Language Medical Information for Patients, Caregivers and General Public: Opportunities, Risks and Ethics’ explains, “AI-based models have the potential to act as a convenient, customizable and easy-to-access source of information that can improve patients’ self-care and health literacy and enable greater engagement with clinicians.”
NLU dictates how these models handle prompts and reasoning tasks. It enables techniques such as few-shot prompting and step-by-step reasoning, where the model uses prior examples or intermediate logic to improve the quality of its output. When combined with approaches like retrieval-augmented generation (RAG), strong language understanding helps reduce errors and unsupported claims by grounding responses in relevant source material rather than relying solely on pattern prediction.
In an inbound security setting, natural language understanding acts as the first layer of analysis for incoming email. It looks beyond surface-level keywords to interpret intent, extract meaningful entities, and assess tone. By breaking messages down into elements such as urgency signals, unusual sender behavior, or subtly manipulative language, NLU can surface risks that traditional rule-based filters often miss.
One recent Inquiry review notes, “NLP and deep learning technologies scan large datasets, extracting valuable insights in various realms. This is especially significant in healthcare, where huge amounts of data exist in the form of unstructured text.”
That deeper understanding is then passed to generative AI systems that can respond in more adaptive ways. These systems can create realistic phishing examples to help train detection models or generate clear explanations that justify why a message was flagged and what action was taken. In more advanced setups, they can even help automate quarantine decisions based on how an attack is likely to unfold.
For example, NLU can pick up on patterns like vague greetings, inconsistent context, or wording that doesn’t match the sender’s usual communication style. Generative models can use those signals to anticipate next steps and simulate how similar attacks have played out in the past. This combination enables the detection of not only individual threats but also coordinated or multi-stage attacks before they fully materialize.
Paubox uses generative AI in its inbound email security solution to provide strong, adaptive protection against phishing, business email compromise (BEC), and impersonation attacks, with a specific focus on the HIPAA compliant email needs of healthcare organizations. Instead of relying on static rules or simple keyword matching, the system analyzes incoming emails as a whole. It combines large language models, vector databases, and generative techniques to evaluate tone, sender behavior, message intent, and historical communication patterns.
The platform to spot subtle warning signs that are easy to miss, like artificial urgency, language that doesn’t match a sender’s usual style, or emails that imitate executive communication patterns. By learning what normal looks like within healthcare-specific workflows, the system can flag suspicious messages with clear confidence scores and explanations that security teams can easily understand and act on.
It identifies subtle linguistic cues, abnormal urgency, and impersonation patterns that traditional filters often miss.
Because it understands language in context, allowing it to detect sophisticated and previously unseen attacks that do not rely on known keywords or signatures.
Yes, it can recognize impersonation attempts and anomalous communication patterns that commonly signal BEC activity.