Cybersecurity leverages a wide range of tools to stay a step ahead of threat actors. GPT is the latest tool to be added to the defense strategy. How can it be deployed to improve all facets of cybersecurity strategies? Greg Hatcher, founder of White Knight Labs, explains how.
The field of cybersecurity is constantly evolving to meet an ever-expanding list of potential threats. Over the past decade, those charged with protecting organizations from cyberattacks have been forced to keep pace as malware evolved into spyware, which subsequently evolved into ransomware. With each new iteration, new complexities and capabilities in the area of cyber threats have demanded new cybersecurity strategies and controls.
One of the most recent developments in cybersecurity involves the use of the generative pre-trained transformer, also known as GPT, to thwart attacks. GPT, which is a type of artificial intelligence (AI), began making headlines in early 2023 when the now-famous chatbot ChatGPT experienced a surge in popularity. As more people experimented with the generative AI upon which ChatGPT is built, it became clear that it could be leveraged as a powerful tool for cybersecurity.
What Is the Technology Behind GPT?
In essence, GPT is a generative pre-trained transformer that leverages machine learning to carry out natural language processing tasks. In simple language, they translate human language into something that a computer can process. An email filter, which sorts incoming messages into distinct inboxes based on their content, is an example of the way in which natural language processing can be used to automate and streamline tasks.
GPT is considered â€œpre-trainedâ€ because it is taught to process language through exposure to large amounts of text data. This training empowers GPT to predict the next word in a sentence based on the words that have preceded it. Because it is built on a transformer model, which allows for the entire input to be processed all at once instead of one word at a time, GPT dramatically increases the speed and efficiency of language training.
How Can GPT Be Used to Thwart Adversarial Attacks?
Adversarial attacks are those which target AI-based systems. Their goal is to manipulate the system into providing sensitive information, influence them in a way that results in incorrect predictions, or corrupt them. A â€œpoisoning attackâ€ is a common type of adversarial attack that attempts to contaminate the data that is being used to train a GPT system.
To protect against adversarial attacks, GPT can be trained on adversarial examples and deployed to detect and deflect them. This involves providing it with large datasets representing the types of adversarial attacks that are being used to defeat cybersecurity efforts. Because it is a machine learning model, the more GPT is used to repel attacks, the more capable it becomes.
Can GPT Be Used to Repel Other Types of Attacks?
GPT can also be trained to detect cyber attacks by identifying anomalies in network traffic data. To serve as this type of defense, the GPT platform must first understand the normal patterns of network activity so that, when it detects deviations in activity, it can trigger the appropriate pre-determined response.
GPT’s capability as a natural language processor also makes it valuable as a tool for detecting and addressing social engineering attacks involving a threat actor targeting an organization’s employees rather than its computer systems. The attacker, posing as a coworker or official representative of a legitimate organization, sends an email or text message to the employee that seeks sensitive information. If the employee unwittingly provides the information, the organization’s computer system can be compromised.
GPT can bolster an organization’s defenses against social engineering attacks by being trained to detect common attacks, patterns of malicious behavior, and suspicious anomalies. When it detects such attacks, GPT can alert the intended recipient to the potential threat. In some cases, GPT can identify and quarantine the attacks before they get to the targeted employee.
Malware attacks often involve strategies that are similar to social engineering. By deceiving a system user, threat actors are able to inject malicious programs into a user’s computer or an organization’s network. GPT can be trained to identify the patterns that are used in malware attacks.
How Can GPT Be Used for Threat Hunting?
In addition to being trained to repel known attacks, GPT can also be used for threat hunting. By analyzing data that is collected by cybersecurity systems, GPT can identify patterns of activity that reveal malicious behavior trends as well as the vulnerabilities they are seeking to exploit. Armed with this information, cybersecurity teams can improve defenses and prevent attacks before they occur.
Password and account security is one area where GPT can be used for threat hunting. By analyzing the passwords being used in a system, GPT can reveal those that are weak to attacks and draw upon its learning to generate passwords that have a higher likelihood of remaining secure. It can also assess network activity data to detect areas where attackers have attempted to hack into accounts.
Threat hunting, which can be an arduous and time-consuming task for cybersecurity professionals, becomes easier and more effective when carried out by GPT. In addition, it releases professionals to focus on more complex tasks. This capacity for automating laborious cybersecurity measures is one of the key benefits that GPT brings to the area of cybersecurity.
Can GPT also Empower Threat Actors?
It should come as no surprise that the power GPT brings to cybersecurity can also be used by threat actors to carry out more effective attacks. One simple application is using GPT to generate realistic text-based attacks, such as phishing or smishing attacks. This can streamline the processes used to launch social engineering or malware attacks, allowing threat actors to deploy more attacks with fewer resources.
In addition, cybersecurity professionals should be aware that GPT can present new weaknesses to security systems if not properly guarded. As already mentioned, GPT systems can be poisoned by attackers who maliciously insert incorrect or biased data into the training process, resulting in a system that produces incorrect or unintended responses. If threat actors get access to the data that was used to train GPT, they can gain intelligence that allows them to design effective attacks.
GPT promises to bring revolutionary change to the cybersecurity realm. Whether it is used for streamlining data analysis, improving threat detection, or simply automating routine tasks, GPT will provide a powerful tool to cybersecurity professionals. The key, as always, will be staying a step or two ahead of those who will seek to use those same capabilities to undermine security.
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