How to apply natural language processing to cybersecurity
Posté le 4 mars 2025 dans AI News par Isidore Monzongoyi.
What Is Natural Language Processing?
NLP, he says, can help automate tasks such as customer support through chatbots, sentiment analysis for market research, and efficient document processing, thereby improving efficiency and enhancing customer engagement. He argues that it is these LLMs that have pushed NLP to new heights, enabling machines to generate coherent, human-like text, summarise long documents, translate between languages, and even engage in meaningful dialogue. By leveraging these models, NLP can now do things that seemed impossible a few years ago, like writing essays or answering complex customer inquiries in a natural, flowing manner.
- Some AI scientists have analyzed some large blocks of text that are easy to find on the internet to create elaborate statistical models that can understand how context shifts meanings.
- Grammarly, for instance, makes a tool that proofreads text documents to flag grammatical problems caused by issues like verb tense.
- The intent with NLWeb is to build a contextual semantic search layer for any website.
- Technologies like Model Context Protocol (MCP) are reshaping both how we build AI-powered applications and what we can do with them.
Listing 7. Detecting sentences
Many chatbots are text-based, interacting with users via instant messaging or SMS, but some use voice and even video. The goal is now to improve reading comprehension, word sense disambiguation and inference. Beginning to display what humans call “common sense” is improving as the models capture more basic details about the world. Clement Delangue is the co-founder and CEO of Hugging Face, a startup focused on natural language processing that has raised more than $20M.
As digital interactions evolve, NLP is an indispensable tool in fortifying cybersecurity measures. The cross-industry set of partners underscore the ambition behind NLWeb, building a tool that quickly extends any web application with natural language interactions. A tool like this needs to be as open as possible, both to be part of the web and to build on the web’s openness. Hopefully, as NLWeb evolves, Microsoft and its partners will standardize the APIs, passing them on to a body like the W3C to ensure widespread adoption. If you want to explore the service in more detail, NLWeb offers a REST API that can be used for queries (/ask endpoint) or as an MCP server (/mcp endpoint) for use in agent-to-agent applications. The API arguments are the same for both endpoints; the only difference is the format of the returned object, which for MCP calls is a JSON object in MCP format.
Natural language processing software
Thanks to AI technologies such as machine learning, coupled with the rise of big data, computers are learning to process and extract meaning from text – and with impressive results. Natural language is used by financial institutions, insurance companies and others to extract elements and analyze documents, data, claims and other text-based resources. The same technology can also aid in fraud detection, financial auditing, resume evaluations and spam detection.
Second, the web, mostly composed of text, provided a large, open and diverse dataset. Sites like Wikipedia and reddit provided gigabits of text written in natural language, which allowed these gigantic models to be properly trained. The significance of the CEO of one of the largest companies in the world publicly talking about natural language processing (NLP), the field of artificial intelligence which applies to text, didn’t go unnoticed.
Training a name model is out of scope for this article, but you can learn more about it on the OpenNLP page. He adds that to improve the accuracy of the responses, NLP leans on machine learning techniques, such as deep neural networks, and models like transformers such as BERT. Unlike traditional computing, which relies on straightforward commands, NLP involves teaching machines to grasp the subtleties and quirks of human language, including context, tone, and meaning, says Sohal. It’s how AI moves from rigid rule-following to more intuitive understanding, opening up new ways for tech to interact with us in a more “human” way.
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NLP can deliver results from dictation and recordings within seconds or minutes. DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation. This is an example from my own experience of the benefits of using cognitive agents to improve customer satisfaction and reduce employee turnover. The search engines have become adept at predicting or understanding whether the user wants a product, a definition, or a pointer into a document. This classification, though, is largely probabilistic, and the algorithms fail the user when the request doesn’t follow the standard statistical pattern. Nori Health intends to help sick people manage chronic conditions with chatbots trained to counsel them to behave in the best way to mitigate the disease.
NLP and LLMs
- The partners comprise an interesting cross-section of the modern web, and it will be interesting to see how uptake develops.
- A bipartisan panel of voters weighed in on the future of artificial intelligence and growing concerns surrounding the potential dangers of the emerging technology.
- Phrases, sentences, and sometimes entire books are fed into ML engines where they’re processed using grammatical rules, people’s real-life linguistic habits, and the like.
These include the OpenAI codex, LaMDA by Google, IBM Watson and software development tools such as CodeWhisperer and CoPilot. The idea of machines understanding human speech extends back to early science fiction novels. Natural language processing (NLP) is a branch of artificial intelligence (AI) that focuses on computers incorporating speech and text in a manner similar to humans understanding. This area of computer science relies on computational linguistics—typically based on statistical and mathematical methods—that model human language use. Natural language interfaces are the next step in the evolution of human-computer interaction, from simple tools to machines capable of event-driven and automated processes, potentially even leading to a kind of symbiosis between humans and machines. This is a fundamental challenge in the grand pursuit of generalizable AI—but beyond academia, it’s relevant for consumers, too.
At the same time, unlike chatbots, responses are returned as JSON objects, giving you control over what is displayed and how it’s formatted. You can also take advantage of technologies like the common entity descriptions offered by Schema.org. Explore the future of AI on August 5 in San Francisco—join Block, GSK, and SAP at Autonomous Workforces to discover how enterprises are scaling multi-agent systems with real-world results. A hotel chain employed a team of 240 customer care agents to deal with over 20,000 customer interactions per day, including phone calls, email, and social media.
