Generative AI in Cybersecurity: 3 Positive Uses and 6 GenAI-Based Attacks

How Is Generative AI Changing the Cybersecurity Field? 

Generative AI is transforming cybersecurity by enhancing defense mechanisms and providing new security strategies, while simultaneously providing new tools for threat actors. 

On the defense side, generative AI can analyze vast amounts of data to identify patterns and anomalies indicative of potential threats. By automating deep analysis of network traffic and system logs, generative AI enables a more proactive approach to cybersecurity, identifying and mitigating threats before they can cause significant harm. Generative AI can also be used to simulate complex attack scenarios and automate security processes, enabling rapid incident response.

Conversely, the same technologies can be leveraged by threat actors to develop more advanced and deceptive attacks. For instance, AI can be used to create highly convincing phishing emails that are difficult for traditional filters to detect. Additionally, AI-generated malware can adapt and evolve, making it harder for security systems to identify and neutralize. 

The dual-use nature of generative AI means that while it provides powerful tools for improving cybersecurity, it also increases the sophistication of potential threats, necessitating continuous advancement in defensive strategies.

This is part of a series of articles about AI security.

Positive Uses: How Is Generative AI Used in Cybersecurity?

1. Enhanced Threat Detection 

Generative AI enhances threat detection by leveraging its ability to analyze vast datasets and identify anomalies that signify potential cyber threats. This capability allows for the early detection of sophisticated attacks, including zero-day threats, by recognizing patterns or behaviors that deviate from the norm.

Generative AI’s application in threat detection extends beyond traditional perimeter defenses. It aids in uncovering subtle indicators of compromise within network traffic and system logs, often invisible to conventional detection tools. It can perform deep analysis, similar to that carried out by human analysts, which provides a more nuanced understanding of threat actors’ tactics, techniques, and procedures (TTPs).

2. Automated Security Measures 

Automated security measures powered by generative AI streamline the implementation of cybersecurity protocols, minimizing the need for manual intervention. These technologies enable the creation of dynamic defense mechanisms that adapt in real-time to evolving threats. 

For example, generative AI can be used to automate incident response processes. It can analyze incidents as they occur, prioritize threats based on their severity, and even generate response playbooks with automated countermeasures. This rapid response capability significantly reduces the window of opportunity for attackers to exploit vulnerabilities.

3. Innovative Problem-Solving 

Generative AI automates problem-solving capabilities in cybersecurity, enabling the development of creative solutions to complex security challenges. By simulating a variety of cyberattack scenarios, generative AI models can help identify potential vulnerabilities in an organization’s network infrastructure that might not be evident through conventional testing methods.

Additionally, generative AI can assist in crafting tailored security strategies that address the unique needs and risk profiles of different organizations. It can analyze past incidents and current threat landscapes to recommend customized measures that effectively mitigate risks.

GenAI-Based Attacks: How Is Generative AI Used by Threat Actors?

While genAI technologies can be used as part of an organization’s cyber defenses, attackers can also use generative AI to power more sophisticated cyberattacks.

1. LLM-Based Phishing and Business Email Compromise 

Threat actors exploit Large Language Models (LLMs) to conduct sophisticated phishing and Business Email Compromise (BEC) attacks. They can generate deceptive emails that are contextually relevant, personalized, and grammatically flawless, mimicking legitimate communications from trusted sources, such as company executives or business partners.

The 2023 Verizon Data Breach Investigations Report highlights this trend, noting a near doubling of BEC incidents, which now represent over half of all social engineering attacks. The ease with which LLMs can be used to craft convincing fake emails poses a challenge for traditional security measures. 

2. AI-Generated Images 

Generative adversarial networks (GANs) and diffusion-based text-to-image models enable the creation of realistic visuals, ranging from human portraits to complex physical scenes. Threat actors, including those aligned with nation-states and independent groups, can craft images that bolster false narratives or impersonate real individuals, helping spread disinformation. 

The accessibility of tools like thispersondoesnotexist.com has led to widespread use of generative AI images in information campaigns. Advancements in text-to-image technology promise even broader applications for deceptive content that is challenging to detect.  

3. Manipulated Video Footage 

AI technologies can be used to manipulate video footage for disinformation campaigns. This includes customizable AI-generated avatars and deepfake technology that alters existing videos to superimpose faces or mimic voices. Such manipulations have been used to fabricate narratives or impersonate individuals, enhancing the persuasive impact of false information. 

The use of manipulated video footage in cyber operations has been observed since 2021, with instances including AI-generated news presenters and deepfake videos of political figures. In 2024 and beyond, with the launch of the first generative AI tools able to generate novel, photorealistic video footage, the threat will become even more potent.

4. AI-Generated Audio 

AI-generated audio, while not yet widely adopted, has shown potential for misuse in creating deceptive content. Technologies enabling text-to-voice generation and voice cloning can produce audio tracks that convincingly mimic public figures, making statements that are violent, racist, or otherwise harmful. 

These capabilities raise concerns about the ease with which individuals could be impersonated to spread disinformation or incite conflict. Despite the limited use observed so far, tools capable of generating realistic voice recordings from text inputs offer a new vector for social engineering attacks and misinformation campaigns.  

5. Improved Reconnaissance 

Generative AI enhances reconnaissance efforts, enabling threat actors to analyze and process vast amounts of open-source and proprietary data. With machine learning and data science tools, adversaries can quickly sift through stolen information or publicly available data to identify valuable targets or vulnerabilities. This improves precision in selecting targets. 

AI-based tools also assist in refining tradecraft techniques used during reconnaissance. For example, AI can uncover patterns in data that human analysts might overlook, providing insights into more effective ways to approach intelligence gathering or target selection. 

6. LLM-Guided Malware Development 

LLMs can assist in creating new malware or enhancing existing ones, irrespective of an attacker’s technical skill or language proficiency. While there are limitations in LLMs’ malware generation capabilities that might require human correction, their contribution to malware creation aids skilled developers and empowers those with lesser expertise.

Reports indicate that financially motivated actors are promoting services on underground forums to bypass LLM restrictions designed to prevent their use in developing and spreading malware, as well as creating deceptive lure materials. Advertisements for LLM services, sales, and API access, as well as LLM-generated code, are increasingly seen on dark web forums.

Best Practices for Using Generative AI in Cybersecurity

Here are some of the ways organizations can make effective use of generative AI advances in their cybersecurity programs.

1. Combine AI and Human Detection in Incident Response 

AI-driven tools can quickly sift through vast amounts of data, identifying potential security incidents with speed and precision. This allows for the rapid detection of anomalies that might indicate a cyberattack, reducing the time between breach discovery and response. However, AI alone cannot fully grasp the context or nuance of every alert, so human review is still important.

Human experts bring critical thinking and contextual understanding to the incident response process, evaluating AI-generated alerts to determine their validity and severity. By combining AI’s ability to process and analyze data at scale with humans’ ability to understand complex scenarios and make decisions, organizations can make their security posture more responsive. 

2. Use Generative AI for Malware Analysis

Generative AI can significantly enhance malware analysis by creating artificial malware samples based on known attack vectors and vulnerabilities. This allows researchers to observe how these samples interact with systems, exploit vulnerabilities, and propagate, all within a secure sandbox environment. By studying these behaviors, cybersecurity professionals can gain deeper insights into the tactics, techniques, and procedures (TTPs) used by cybercriminals, thereby improving their understanding of evolving threats.

Additionally, GenAI-generated malware can be used to train cybersecurity teams, enhancing their ability to recognize and respond to new and sophisticated threats. By simulating real-world attack scenarios, teams can practice their response strategies and improve their incident handling skills. This proactive approach ensures that organizations remain one step ahead of cyber adversaries, better prepared to defend against potential breaches.

3. Use Generative AI to Create Missing Patches for Vulnerabilities

Generative AI streamlines the process of identifying, creating, and testing security patches for software vulnerabilities, significantly reducing the time and effort required. When a critical vulnerability is discovered, GenAI can quickly analyze the issue, generate code for a customized patch, and even test its effectiveness in a controlled environment. This rapid response capability helps mitigate risks before they can be exploited by threat actors.

By automating the patch generation process, organizations can maintain a higher level of security and reduce the window of opportunity for attackers to exploit known weaknesses in their systems.

4. Use AI-Powered Email Security Solutions 

AI technologies can enhance protection against sophisticated email threats. For example, image recognition algorithms identify and analyze visual content, helping defend against phishing attempts and brand spoofing. Natural Language Processing (NLP) models understand organizational communication patterns, detecting social engineering attacks. 

Behavioral and content analysis further strengthens email security by identifying anomalies and malicious intent in messages. This approach analyzes changes in communication tone, metadata, and the presence of sensitive information, offering a nuanced detection capability that adapts to evolving threats.  

Leveraging Generative AI in Cybersecurity with Perception Point

Perception Point uniquely combines an advanced AI-powered threat prevention solution with a managed incident response service to protect the modern workspace. By fusing GenAI technology and human insight, Perception Point protects the productivity tools that matter the most to your business against any threat. 

Patented AI-powered detection technology, scale-agnostic dynamic scanning, and multi-layered architecture intercept all social engineering attempts, file & URL-based threats, malicious insiders, and data leaks. Perception Point’s platform is enhanced by cutting-edge LLM models to thwart known and emerging threats.

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