How Can Generative AI Be Used In Cybersecurity: Use Cases, Risks & More

Summary!

Thinking about how can generative AI be used in cybersecurity? Don’t worry, we’ve got you! Many call these digital transformations a double-edged sword that is capable of both securing and harming one’s data, processes, and privacy. 

Generative AI in cybersecurity can help with the former, and that’s what we will talk about today. This blog will focus on the role of generative AI in cybersecurity and why it is needed. How it can enhance the business workflow, along with associated risks and ethical concerns. So, let’s get started. 

Everyone is embracing the digitally evolving ecosystem, but with caution. While it is no lie that AI and its transformative capabilities have revolutionized the way we interact with the world, this shift also raises concerns regarding cybersecurity. 

Now, fraudulent activities are not limited to shady phone calls or OTPs. Fraudsters take many forms to trick innocent people, such as committing ransomware, phishing, zero-day exploits, etc. Such cyberattacks are becoming increasingly advanced, which adds to the concerns as AI continues to advance and penetrate various business processes. 

With generative AI, these attacks can be prevented and handled proactively. Generative AI monitors, observes relevant patterns, and detects anomalies faster than humans. It is also capable of making decisions autonomously, when needed, to reduce response time to cyber threats. There are many such generative AI applications in cybersecurity. But before we get into that, let us understand a few things:

What is Generative AI? 

Generative AI is a subset of artificial intelligence that relies on advanced architecture to create new textual, visual, video, and audio content based on human input that acts as the trigger. The GenAI system also consists of many advanced technologies, like;

• Machine Learning (ML) – To help the model learn from training data to make predictions and detect anomalies. 
• Large Language Model (LLM) – To analyze large volumes of data, detect phishing emails, and automatically report on any incident (if it occurs). 
• Generative Adversarial Network (GAN) – To stimulate and assess the defense system so it can be proactively updated to prevent any (possible) cyberattack.
• Variational Autoencoder (VAEs) – To detect subtle data anomalies, which primarily helps safeguard financial transactions against cyber attacks.

Want to read how AI can secure crypto transactions? Check out our case study on how we created an AI-based fraud detection system for secure transactions. 

Generative AI has a specific function, i.e., to create new content based on user input. It generates text, images, (sometimes) videos, and code when the user asks for it. On the contrary, Artificial Intelligence has much broader use cases but mainly focuses on process optimization via roadblock analysis and automation. 

In essence, artificial intelligence is a broader concept encompassing many digital advancements with specific functionalities, including generative AI, which generates content. 

Why There’s a Need for AI in Cybersecurity 

First things first, what are the limitations of the current operating system that AI can resolve? Or, what is the need to implement AI in cybersecurity? 

Here are a few challenges in cybersecurity that AI helps overcome: 

Limited Capabilities of Traditional Rule-Based Systems 

The traditional system relies on predefined rules to identify malware and threats. These systems can catch attacks with clear patterns or known signatures. However, they fail in the case of new and evolving ones. Generative AI-based cybersecurity systems learn, adapt, and identify unknown threats, making them ideal for today’s world. 

Threat Frequency Makes Manual Analysis Redundant

With thousands of alerts flooding the system on a daily basis, manual assessment of every fraudulent activity will be more time-consuming and prone to errors. Generative AI can filter out false positives and help human teams focus on actual threats. GenAI mitigates damages through automated actions and reduced response time. 

Sophisticated Cyber Threats Require Intelligent Analysis

Fraudsters are employing AI to improve their attacks, exploiting unknown or overlooked vulnerabilities that would pass through the existing or traditional monitoring systems. GenAI significantly improves the defence mechanism by stimulating potential attacks, thus preparing the system to react in case of such threats, in case they actually happen. 

Shortage of Skilled Cybersecurity Professionals

With a lack of qualified resources for the profession, it is difficult to recruit and retain the right talent. AI bridges this gap by assisting the existing team of cybersecurity professionals by assessing a large volume of data to filter out actual threats. This will accelerate the process, simplify it, and improve decision-making. 

Five Generative AI Applications in Cybersecurity

To answer the initial question, “How can generative AI be used in cybersecurity?” Here are five GenAI applications in cybersecurity. Integrating GenAI in the existing system can significantly enhance the defense mechanism through real-time analytics, automated operations, and more. To elaborate on generative AI applications in cybersecurity:

#1 Threat Detection and Response Automation

GenAI uses machine learning to analyze and understand patterns, which makes it easier to detect system discrepancies. AI removes the false positives, thus filtering out the attacks for the cybersecurity team to act upon. 

Generative AI also provides strategic steps to prevent cyberattacks based on previous data. Additionally, since GenAI is powered by ML, it will utilize the data at hand to evolve and adapt its response strategies, thus ensuring long-term protection against cyber attacks. 

#2 Phishing Detection 

Large Language Models can analyze vast volumes of data and capture anomalies in minutes. Phishing or fraud mail detection helps combat the most common form of cyber attack, where the fraudster attempts to steal money by pretending to be a banking service provider.  

LLM-powered GenAI system identifies minute anomalies, such as sender address, errors, suspicious links, etc., and triggers alerts immediately in case of potential phishing attacks. This safeguards users against financial loss or data breach. 

Related Read – LLM Security: Protecting AI Models from Attacks & Data Leaks 

#3 Security Policy Documentation 

One of the most popular use cases of generative AI is summarization. In cybersecurity, it helps with the proper documentation of guidelines, procedures, previous attacks, preventive measures, etc. ML and LLM are used to generate a thorough report in natural language. 

This is beneficial for both record-keeping and as a future reference. This accelerates the process by saving the time in searching for the best recovery route. A detailed account of the architecture will be beneficial for future updates. 

#4 GenAI-Based Defense Simulation 

Generative AI in cybersecurity is also leveraged for its original content creation abilities. These models create malware by using polymorphic codes in a controlled environment. This helps the team assess potential threats securely.  

Generative AI facilitates DevSecOps teams by identifying vulnerabilities and security gaps before deployment. It mimics the actual threats but offers a controlled environment for the system to understand and evolve accordingly, thus employing a proactive security approach against advanced threats.

Related Read – DevOps vs DevSecOps: Choosing the Right Fit for Your Organization

#5 GenAI-Powered Attack Simulation 

Also known as AI Red-Teaming, attack simulation uses GenAI to mimic hacker behavior and test system resilience. It uses GANs and LLMs to design phishing emails, bypass authentication, and identify any weaknesses. 

Since AI is able to better adapt and scale to newer environments, it easily adjusts to and for the new attacks. Realistic attacks allow organizations to uncover hidden vulnerabilities and handle them before actual cyber attacks hit them. 

Advantages of Generative AI in Cybersecurity

These are the benefits that businesses get to leverage by integrating generative AI in cybersecurity: 

Enhanced ROI 

Generative AI in cybersecurity helps prioritize resource allocation. It ensures optimal asset protection without exceeding the budget, depending on the risk associated. Organizations utilize GenAI to design appropriate investment plans per their requirements. 

Proactive Defense 

Generative AI’s advanced capabilities facilitate automated incident response and real-time alert management, significantly reducing business downtime and costs. These features allow businesses to take preventive measures through regular audits and mitigate cybersecurity breaches. 

Improved Operational Efficiency

Generative AI improves processes by automating repetitive tasks so the security team can focus on more valuable tasks. This reduces manual labor and time invested in tasks such as documentation, while accelerating processes. 

Competitive Advantage

Integrating advanced AI security solutions for protection while others are still figuring out how can generative AI be used in cybersecurity is a huge competitive advantage. This secures your data and ensures your clients that you value digital security, thus establishing strong trust.

Real World Use Cases & Examples of GenAI in Cybersecurity

In the section above, we talked about generative AI applications in cybersecurity. Now, let us have a look at some real-world examples and use cases of this intelligent technology in cybersecurity: 

Gmail’s Phishing Email Detection 

With millions of users, the Gmail platform encounters multiple phishing attacks daily. Google utilized ML models to improve phishing detection. The model is trained on various relevant datasets, including email content, sender information, etc. 

Darktrace Threat Detection 

The leading cybersecurity firm, Darktrace, utilizes deep learning to improve threat detection capabilities. This prevents malware from passing through the system using advanced ML algorithms and autonomous threat responses. 

Facebook’s Social Media Monitoring 

Facebook, the leading social media platform utilizes NLP to asses its posts, comments, and messages for harmful content. These models use set keywords and phrases to identify negative, aggressive posts and flag them to mitigate harmful and false rumors from spreading.  

Risks & Concerns of Using Generative AI in Cybersecurity

While generative AI is a key element in improved cybersecurity, it is essential to note that the same technology powers sophisticated cyberattacks. Thereby, knowing potential risks and ethical considerations of generative AI in cyberattacks. 

This ensures accountability and helps the organization take preventive measures before AI turns destructive. To list out some of the key risks and ethical considerations associated with GenAI in cybersecurity are: 

What Risks are Associated with the Use of Generative AI in Cybersecurity?

Hackers Misusing GenAI 

Artificial intelligence is available to everyone, whether with good intentions or bad, people can use or misuse AI. While organizations and cybersecurity officials use GenAI to protect, hackers can use this technology to design advanced cyber threats like deepfakes and synthetic phishing. 

False Positives & Hallucinations

When the AI models are trained on poor data or are not well-maintained, they can pose a massive problem by either alerting about false positives or being unable to detect actual threats. This can compromise critical data and cause major security issues. 

Over-Reliance on AI Results 

Artificial intelligence, as advanced as it is, can and sometimes does fail. Ignoring these things can compromise an organization’s critical data. Not only that, but organizations that rely on AI for protection against cyberattacks can raise many serious ethical concerns as well. 

What are the Ethical Considerations of Using Generative AI in Cybersecurity?

Legal Issues & Copyright 

GenAI requires multiple data sources to maintain output accuracy. At times, the actual source is unknown, which can lead to ethical complications. Organizations must cross-verify the AI output to avoid potential copyright infringement that may cost businesses their reputation and fortune. 

Sensitive Data Confidentiality 

GenAI or AI in general has made data more accessible than ever, which is good. However, it can unintentionally compromise sensitive information if not kept in check. Businesses must employ clear, ethical guidelines and regularly conduct audits to check for discrepancies.

Offensive vs Defensive AI 

It is a known fact that AI can defend (proactively protect the system) and offend (improve cyberattacks). Both cases raise ethical concerns regarding their misuse, system breach, data privacy, or algorithmic bias, which can be regulated through consistent surveillance and improvement. 

AI Governance & Compliance 

With AI becoming intelligent with every update, it is important to restrict it behind the regulatory guidelines designed by the government. This step will ensure that the autonomous solutions aren’t used to fabricate, alter, or eliminate any information with ill intentions.

What is the Future of Generative AI in Cybersecurity?

Many new avenues will unfold as generative AI grows, evolves, and expands its capabilities. Businesses will opt for generative AI to improve their security systems, leaving behind doubts and apprehension about the up-and-coming technology. It will solidify its footing in securing the organization against digital theft. 

AI will lead to many new opportunities, some of the prominent ones being: 

• Autonomous Cybersecurity Agents: AI agents will be able to detect, analyze, and act on threats independently, thus reducing response time with adaptive defense. 
• AI + Human Collaboration: AI will provide meaningful assistance through actionable insights, automated routing, and strategic assessments to improve operational efficiency. 
• AI-as-a-Service for Cybersecurity: Cloud-based AI solutions will make generative AI in cybersecurity accessible, allowing businesses to integrate scalable solutions on demand. 
• Predictive Security: With further advancements, GenAI-powered cybersecurity solutions will be able to forecast attacks and foster proactive defense in an evolving landscape. 
• Stricter Regulatory Frameworks: In the upcoming years, more thorough and comprehensive AI guidelines will be introduced to ensure transparency for unbiased and auditable outputs. 
• Quantum Computing: AI models will evolve into highly secure protocols to identify encryption flaws and enhance threat detection with quantum processing.

Final Thoughts on How Can Generative AI be Used in Cybersecurity? 

With generative AI taking the leading stage in the current transformative ecosystem, businesses from across the globe need to invest in these modern technologies. This is to ensure that their critical processes are guarded by multiple layers of security that evolve to protect their infrastructure. 

Given the ethical considerations and challenges that revolve around integrating generative AI, the doubts and second thoughts are more than just hindrances in adopting this modern technology. However, with it becoming a necessity, businesses must consult with a reliable AI development services provider. 

At Openxcell, we ensure that the client’s requirements and secure development practices lead the development process. This allows us to transform business ideas into robust solutions that help businesses evolve, expand, and upscale while securing their infrastructure.