Since late 2022, it seems everyone knows what ChatGPT is. What many people do not know is that the popular chatbot is based on a technology known as GPT that has been making waves for a while now. So, what is GPT? Here is a guide to what you need to know about this important technology.

GPT stands for Generative Pre-trained Transformer, which is an artificial intelligence (AI) language model developed by OpenAI. GPT is designed to understand and generate text that mimics human language. It is “pre-trained“ because the model was trained on a large dataset before its initial deployment.

GPT uses a deep learning architecture known as a transformer. Transformers can process and generate sequential data. This makes GPT effective at processing language. That is how you can have a conversation with ChatGPT and why GPT is also used for cybersecurity purposes.

GPT has proven proficient at a number of security tasks, including phishing detection, threat prevention, social engineering awareness, and log analysis. Here is a look at how GPT has become an important tool for security teams.

At the core of GPT architecture is the Transformer deep-learning model. What sets Transformer apart from other models is its self-attention mechanism. This gives it the ability to weigh the importance of each word in a sentence based on their relevance to each other. The self-attention mechanism is applied in layers, each consisting of sub-layers. These sub-layers operate in what is known as a neural network. 

Neural networks are a type of algorithm that operates much like the human brain, giving AI based on them their “human-like“ capabilities. The basic building block of a neural network is an artificial neuron, which receives inputs, performs computations and produces an output. Each output is adjusted through an iterative learning process during training until the model reaches the desired function. In the case of Transformer and GPT, that desired output is a human-like capability to process language.

GPT utilizes machine learning models to train itself in different stages. In pre-training, GPT is fed a large amount of text, mainly collected from the internet. Machine learning helps GPT learn to predict the next word in a sentence or fill in missing words by analyzing human speech patterns. These models also power the GPT fine-tuning phase. This allows GPT to do specific language tasks, such as answering questions or summarizing concepts.

OpenAI has released several iterations of GPT, beginning with GPT-1. GPT-2 brought significant improvements to its language capabilities. GPT-3 featured its largest model up to that point, with 175 billion parameters. Following a fine-tuning with GPT 3.5, GPT-4 was released in March 2023. OpenAI says GPT-4 is 10 times more advanced than GPT-3.5 and is better able to understand context and nuances.

With its ability to process language and context, GPT has become a valuable tool in the cybersecurity community, particularly for security operations (SecOps) teams.

The enhanced data analysis in GPT allows it to process security reports, articles, and research papers. It can extract relevant information from text, identify security trends, and quickly analyze relationships within the data. The information returned by GPT helps SecOps make informed decisions and identify vulnerabilities faster than with human analysis alone.

This has helped GPT play a significant role in email security. GPT analyzes email headers, content, and attachments to detect potentially dangerous patterns. The model accurately identifies phishing emails, spear-phishing attempts, and malware in attachments, filtering them all before a message even makes it to an inbox.

The speed and accuracy at which GPT can perform these functions have made it a critical component of modern SecOps. By automating repetitive tasks and providing real-time security guidance, SecOps teams have become more efficient than ever.

In particular, GPT is excellent at analyzing security logs and identifying patterns for potential threats. It can even prioritize tasks based on severity, helping human security professionals work more efficiently.

With all these capabilities, GPT has also automated many SecOps practices. It can read logs, generate security reports, and offer recommendations on security measures; it can even perform automated responses to security threats. For example, if GPT were to detect an incoming distributed denial of service (DDoS) attack, it could initiate the process of shutting down a firewall.

Analysis of logs and other security data can also be automated with GPT. Since Transformer and GPT machine models are always learning, GPT delivers fresh insights of the security landscape.

Even though GPT has been an invaluable cybersecurity tool, particularly in email security, it has limitations.

Some observers have noted a bias that likely comes from training data: GPT is trained on large datasets from the internet. As such, any biases present in the data will be incorporated into GPT and its decision-making process. Security teams should be aware of this and address biases when found.

GPT, like other tools based on machine learning models, is susceptible to adversarial attacks. Input data can be intentionally manipulated to deceive GPT and cause it to generate malicious outputs. 

Also, its reliance on existing data from the internet means GPT may not be aware of emerging threats. Continuous monitoring, threat intelligence, and integration with real-time security systems can help mitigate this limitation.

Although GPT and similar tools cause concerns about humans being replaced by AI, its lack of human judgment is actually one of its most significant weaknesses: GPT generates responses based on statistical patterns learned without a true sense of context, leading to possible misunderstandings. While GPT is excellent for automating certain repetitive and laborious tasks, it works best in combination with human expertise and judgment. Without a human, it lacks oversight to ensure accurate and appropriate decision-making.

Implementing GPT also requires an investment of time to keep up with the latest developments, research, and best practices in both AI learning models and cybersecurity. This is a relatively new field that is constantly evolving, so new techniques, models, and capabilities are expected to emerge. Stay flexible and be ready to adapt your implementation strategy as new advancements and challenges arise.

Given the many capabilities of GPT, using it as a cybersecurity tool requires some forethought and analysis. Here are some best practices for implementing GPT.

Identify business objectives

Start by clearly defining your security needs and objectives, then determine whether GPT can help. Think of specific use cases, such as threat detection, incident response, or data analysis, where GPT performs well. Make a list of what you would like GPT to do for you or help you with.

Choose the right GPT solution

Learn about the different versions and sizes of GPT models that OpenAI offers before making any purchase decisions. After you narrow down the specific capabilities that are required for your cybersecurity strategy, look for products that use GPT already and suit your needs. The best security solutions built on GPT take advantage of its analytical capabilities to quickly identify threats. 

Develop a data strategy

Data is crucial to training and fine-tuning GPT models. Define a data strategy that suits your organization and its data collection and analysis processes. The key to a successful implementation is training data representing a wide range of cybersecurity scenarios. You're also looking to be free of biases at this stage, so pay close attention to data quality and relevance. It is also critical to be aware of any potential privacy concerns when collecting and using data for training GPT. Remember that it lacks human judgment on what is appropriate and what is not.

Monitor and evaluate GPT performance

After you have your GPT model up and running, continuously monitor and evaluate its performance. Establish benchmarks to assess the reliability and effectiveness of GPT and regularly review these metrics. You will also want to track its performance and adjust system resources as needed.

One of the best things about GPT is its adaptability to a diverse range of cybersecurity scenarios across many industries. Here are some examples of several industries using GPT to bolster their cybersecurity.

Healthcare: GPT can analyze patterns in electronic health records to detect suspicious activities such as unauthorized access or other data breaches. It can also assist in identifying potential privacy risks and ensuring compliance with patient data regulations like HIPAA.

Finance: Detecting fraud is one of the most common uses of GPT in security contexts today. By analyzing transactions and user behavior patterns, GPT helps financial institutions prevent and mitigate fraudulent activity. The models can also help identify risk by analyzing market data, economic indicators, and financial news. GPT can provide insights into market trends, helping finance teams make informed decisions.

Retail: For e-commerce retailers, GPT can review online data to suss out negative reviews. It is also used to analyze supply chains by looking at vendor information, logistics data, and product tracking.

Education: Student data can be protected with the help of GPT, ensuring privacy and alerting to potential data breaches. GPT can reduce or stop account takeover attempts. It is also useful for ensuring compliance with privacy regulations. 

At Armorblox, GPT plays a prominent role in our products. We offer Advanced Threat Protection, Advanced Malware Detection, automation solutions for SecOps teams, and more features that use GPT and its deep learning, analysis, and learning capabilities.

For instance, Armorblox uses natural language understanding for threat analysis, malware detection, and even in our Advanced Data Loss Prevention product. This keeps your email safe and free from threats and helps protect your organization against accidental or malicious exposure of sensitive data. By learning the historical behavior patterns of users over time, we are able to identify “normal“ business communication and potentially dangerous actions.

We also use GPT predictive analysis to provide a higher level of organizational security. With training on security logs, network traffic, and other cybersecurity data, Armorblox products identify patterns and potential anomalies. We use GPT to recognize known threats and prevent them from making it into your organization.

Armorblox products also utilize GPT user behavior analytics features to understand your organization and its business processes. GPT helps Armorblox make sense of login patterns, file access, and data transfers to understand which are normal behaviors and which could be malicious.

For example, our financial fraud prevention tools not only prevent targeted attacks from the outside world but also stop insider threats. Using GPT and machine learning algorithms, we are able to detect user behavior patterns that may indicate fraud. Analysis of email content, such as a request for a bank account change, can be flagged. 

This high-level understanding of user behavior and business processes helps Armorblox products to discover threat indicators faster. Our analysis of behavior patterns identifies unusual activity quickly.

Armorblox harnesses all of the GPT benefits and makes them available as a service. With Armorblox, you will not need to be an expert in AI and machine learning models to get GPT-powered security for your organization.