Unstructured text data a gold mine for AI language processing

NLP algorithms can scan vast amounts of social media data, flagging relevant conversations or posts. These might include coded language, threats or the discussion of hacking methods. By quickly sorting through the noise, NLP delivers targeted intelligence cybersecurity professionals can act upon. The overlap between NLP and cybersecurity lies in analysis and automation. Both fields require sifting through countless inputs to identify patterns or threats. It can quickly process shapeless data to a form an algorithm can work with — something traditional methods might struggle to do.

NLP And Banking: A Winning Combination For Fraud Detection

It’s where NLP becomes incredibly useful in gathering threat intelligence. “They don’t correspond to the way that people speaking English actually use pronouns,” he wrote in an email. Until pretty recently, computers were hopeless at producing sentences that actually made sense. But the field of natural-language processing (NLP) has taken huge strides, and machines can now generate convincing passages with the push of a button. Begin with introductory sessions that cover the basics of NLP and its applications in cybersecurity.

Here’s the exciting part — natural language processing (NLP) is stepping onto the scene. But she also hopes it will inspire more researchers to look beyond deep learning. The results emphasized to her that true common-sense NLP systems must incorporate other techniques, such as structured knowledge models. NLP can analyze text outside of clinical documents as well, Ng explained.

CLEVELAND — Patients with chronic diseases may have files that include dozens of clinical records that providers may not have time to read thoroughly. Some people believe chatbots like ChatGPT can provide an affordable alternative to in-person psychedelic-assisted therapy. On a daily basis, the insurance industry faces a very high percentage of claims that are likely to be fraudulent. In the U.S., insurance fraud costs $309 billion a year; this equates to almost $1,000 for every single U.S. citizen.

• Relying on machine learning (ML) and NLP can provide better handling of legacy systems as well as siloed data sources.
• Until pretty recently, computers were hopeless at producing sentences that actually made sense.
• From speeding up data analysis to increasing threat detection accuracy, it is transforming how cybersecurity professionals operate.
• As digital interactions evolve, NLP is an indispensable tool in fortifying cybersecurity measures.

Phishing email detection

It’s effective for quick proofs of concept and delivers high returns on investment. This is especially true when NLP models need to understand higher-level items like categories of concepts within documents. NLP offers many benefits that can revolutionize cybersecurity efforts. It’s time to take a leap and integrate the technology into an organization’s digital security toolbox.

This speed enables quicker decision-making and faster deployment of countermeasures. Simply put, NLP cuts down the time between threat detection and response, giving organizations a distinct advantage in a field where every second counts. In a field where time is of the essence, automating this process can be a lifesaver. NLP can auto-generate summaries of security incidents based on collected data, streamlining the entire reporting process. Social media is more than just for sharing memes and vacation photos — it’s also a hotbed for potential cybersecurity threats. Perpetrators often discuss tactics, share malware or claim responsibility for attacks on these platforms.

These industries have a great need to deal with fraud in a proactive and technologically sophisticated way, and they can find a great ally in AI and, more specifically, in solutions based on NLP. Relying on machine learning (ML) and NLP can provide better handling of legacy systems as well as siloed data sources. The recent presentation of the latest version of the OpenAI language model, GPT-4, has raised a wave of expectations. Time is often a critical factor in cybersecurity, and that’s where NLP can accelerate analysis. Traditional methods can be slow, especially when dealing with large unstructured data sets. However, algorithms can quickly sift through information, identifying relevant patterns and threats in a fraction of the time.

How to apply natural language processing to cybersecurity

State-of-the-art deep-learning models can now reach around 90% accuracy, so it would seem that NLP has gotten closer to its goal. This is where NLP comes into play, which facilitates checks on watch lists and sanctions in near real time. Encrypted searches and document processing provide valuable insights for fraud detection and further investigations. By analyzing logs, messages and alerts, NLP can identify valuable information and compile it into a coherent incident report.

The AI insights you need to lead

It creates a user-friendly environment, fostering trust and satisfaction. Elevating user experience is another compelling benefit of incorporating NLP. Automating tasks like incident reporting or customer service inquiries removes friction and makes processes smoother for everyone involved.

This innovative technology enhances traditional cybersecurity methods, offering intelligent data analysis and threat identification. As digital interactions evolve, NLP is an indispensable tool in fortifying cybersecurity measures. As businesses and individuals conduct more activities online, the scope of potential vulnerabilities expands.

• These actionable tips can guide organizations as they incorporate the technology into their cybersecurity practices.
• “What we’re doing is mimicking the behavior of lawyers in everyday work,” James Lee, co-founder and CEO of LegalMation, said during the IBM event.
• The algorithms provide an edge in data analysis and threat detection by turning vague indicators into actionable insights.
• For example, it can be used to learn more about patient experiences through online forums.
• In short, these are two real examples of NLP’s applications in different sectors that expand the security focus of companies.

Zac Amos is features editor at ReHack, where he covers cybersecurity, AI and automation. Data quality is fundamental for successful NLP implementation in cybersecurity. Even the most advanced algorithms can produce inaccurate or misleading results if the information is flawed. Thus, ensuring the input is clean, consistent and reliable is crucial.

Prioritize data quality

Gradually move to hands-on training, where team members can interact with and see the NLP tools. This targeted approach allows individuals to measure effectiveness, gather feedback and fine-tune the application. It’s a manageable way to learn the ropes without overwhelming the cybersecurity team or system. One of the most practical examples of NLP in cybersecurity is phishing email detection.