Generative AI Uses in Cybersecurity
The fast-paced evolution of cybersecurity technology has introduced generative AI which offers promising opportunities yet creates multiple security challenges.
The increasing cyber attacker dependence on advanced technology requires us to understand the dual nature of generative AI.
Hackers employ generative AI to generate fake stories which they use in phishing attacks to deceive their victims. The security threat from cybercriminals grows because ChatGPT and similar tools enable the creation detect security threats and enhance their cybersecurity defences.
The development of ethical hacking through generative AI tools of malicious code. The same technology which hackers use for attacks enables organizations to develop AI-based systems which creates challenges because it requires organizations to establish responsible usage standards.
The future of cybersecurity will depend on how AI systems function between offense and defence because organizations need to develop strategic methods to stop new cyber warfare techniques from emerging.
The image in [image reference] provides visual evidence to help readers better understand the complex situation.
The chart presents multiple performance indicators which describe the generative AI market segment in cybersecurity. The chart presents 2024 market size data along with projected 2034 market growth and calculates the compound annual growth rate (CAGR) from 2024 to 2034. The chart demonstrates that most organizations have started protective measures against generative AI threats and most companies use AI for cybersecurity purposes and security experts report protect against threats. [Download the chart](sandbox:/mnt/data/generative_ai_cybersecurity_chart.png)
How Hackers Use Generative AI for Phishing Attacks: Offensive Applications of Generative AI in Cyber Attacks
The cyber warfare domain continues to transform because cybercriminals now use generative AI as their essential tool to execute sophisticated attacks at high velocity.
The phishing attacks of cybercriminals succeed at a higher rate because they use ChatGPT to generate fake messages which appear authentic to their victims.
The use of Generative Artificial Intelligence (GenAI) by cybercriminals has become more prevalent because it enables them to enhance their methods and perform automated deception operations while overwhelming existing security systems.
AI-powered social engineering examples
Social engineering attacks become more dangerous because AI systems enable personalized interactions with users who speak different languages. The AI-based attack methods create two major security risks because they directly endanger systems and demonstrate existing weaknesses in cybersecurity systems.
The visual representation of AI Uses in Cybersecurity through images demonstrates why organizations need to develop robust security systems against these threats.
The fight between AI-based attacks and defensive measures requires organizations to establish policies for responsible offensive AI use and to implement ethical hacking through generative AI tools.
Best Tools for AI-generated Deepfake Detection
Application | Description | Source |
AI-Generated Phishing Campaigns | Cybercriminals use generative AI to craft personalized phishing emails that closely mimic authentic communications, making detection difficult even for vigilant recipients. These AI-generated messages are often hyper-personalized, complicating detection and mitigation efforts. ([sbir.gov](https://www.sbir.gov/awards/210142?utm_source=openai)) | Jericho Security, Inc. 2024 Award |
Deepfake Technology | Generative AI enables the creation of realistic deepfake videos and manipulated audio, which can spread disinformation and erode trust in institutions. Attackers use these tools to produce convincing fake videos and audio clips, leading to potential reputational damage and misinformation. ([captechu.edu](https://www.captechu.edu/blog/double-edged-sword-how-generative-ai-being-used-create-and-protect-against-cyberattacks?utm_source=openai)) | Capitol Technology University, 2023 |
AI-Generated Malware | Cybercriminals employ generative AI to develop sophisticated malware that can adapt and evade traditional security measures. This includes the creation of polymorphic malware that changes its code to avoid detection by antivirus software. ([mitsloan.mit.edu](https://mitsloan.mit.edu/ideas-made-to-matter/80-ransomware-attacks-now-use-artificial-intelligence?utm_source=openai)) | MIT Sloan, 2023 |
Automated Password Cracking | Generative AI is used to automate the process of password cracking, enabling attackers to quickly and efficiently guess passwords, even those that are complex. This significantly reduces the time required to breach systems protected by weak passwords. ([captechu.edu](https://www.captechu.edu/blog/double-edged-sword-how-generative-ai-being-used-create-and-protect-against-cyberattacks?utm_source=openai)) | Capitol Technology University, 2023 |
AI-Driven Social Engineering | Attackers leverage generative AI to conduct social engineering attacks, such as creating fake customer service calls or messages that appear legitimate, thereby deceiving individuals into divulging sensitive information. ([mitsloan.mit.edu](https://mitsloan.mit.edu/ideas-made-to-matter/80-ransomware-attacks-now-use-artificial-intelligence?utm_source=openai)) | MIT Sloan, 2023 |
Offensive Applications of Generative AI in Cyber Attacks
Defensive AI for Detecting Generative AI Threats
The implementation of generative AI technology in cybersecurity during our modern technological era requires organizations to develop innovative security measures which protect against potential threats.
Using ChatGPT for writing malicious code
The use of generative AI by hackers has become more common because they use ChatGPT to create malicious code for phishing attacks which demands stronger AI-based defensive systems.
Organizations should implement AI-based threat detection systems which use AI to identify generative AI threats to enhance their traditional threat intelligence and vulnerability management capabilities.
How to Use generative AI for Penetration Testing?
Organisations can use ethical hacking methods with generative AI to perform penetration testing which reveals security vulnerabilities before attackers exploit them [cited].
The image demonstrates how AI technology serves multiple functions in cybersecurity operations. The image demonstrates how organisations can stop AI-based cyber threats through their implementation of identity and access management systems and behavioural analytics solutions.
Organisations need to establish policies which support responsible offensive AI operations to achieve security innovation equilibrium in the cybersecurity field.
Image1. Applications of AI in Cybersecurity
 Mitigating Risks of AI in Cyber Warfare
Defensive Strategy | Description | Source |
Adaptive Threat Hunting | Utilizing Generative AI to predict, detect, and dynamically mitigate cyber threats, enabling proactive threat detection within infrastructure to reduce risks and impacts. | University of Kentucky, ‘Revolutionizing Cyber Defense: Leveraging Generative AI for Adaptive Threat Hunting’, Internet Technology Letters, 2025. ([scholars.uky.edu](https://scholars.uky.edu/en/publications/revolutionizing-cyber-defense-leveraging-generative-ai-for-adapti?utm_source=openai)) |
AI-Driven Cyber Threat Intelligence Feed Correlation | Employing Generative AI for accelerated correlation across multiple incoming information feeds from government, commercial, and open sources, enhancing the timeliness and enrichment of cyber threat intelligence. | CISA, ‘CISA Artificial Intelligence Use Cases’, 2025. ([cisa.gov](https://www.cisa.gov/ai/cisa-use-cases?utm_source=openai)) |
AI-Powered Cyber Incident Reporting and Analysis | Applying Generative AI and natural language processing to incident information to increase the accuracy and relevance of data filtered and presented to analysts, assisting in aggregating information for reports and further analysis. | CISA, ‘CISA Artificial Intelligence Use Cases’, 2025. ([cisa.gov](https://www.cisa.gov/ai/cisa-use-cases?utm_source=openai)) |
AI-Enhanced Forensic Investigation | Utilizing advanced forensic investigation analytic tooling powered by Generative AI to analyze cyber events, allowing forensic specialists to detect anomalies in a timely manner. | CISA, ‘CISA Artificial Intelligence Use Cases’, 2025. ([cisa.gov](https://www.cisa.gov/ai/cisa-use-cases?utm_source=openai)) |
AI-Driven Cyber Vulnerability Reporting | Leveraging machine learning and natural language processing to process data received through various vulnerability reporting channels, enhancing the accuracy and relevance of data presented to analysts and decision-makers. | CISA, ‘CISA Artificial Intelligence Use Cases’, 2025. ([cisa.gov](https://www.cisa.gov/ai/cisa-use-cases?utm_source=openai)) |
Defensive Strategies Utilizing Generative AI for Cybersecurity
Ethical Considerations and Policy Frameworks for AI in Cyber Warfare
Ethical Hacking with Generative AI Tools
The fast-paced entry of generative AI into everyday life creates multiple ethical dilemmas which require strong policy structures to address particularly in cyber warfare contexts.
The ethical use of AI for offensive purposes becomes increasingly important when cyber attackers employ generative AI techniques to create fake code and sophisticated phishing attacks.
The practice of ethical hacking with AI tools requires thorough examination to achieve proper safety measures while allowing technological progress.
Organizations must acquire top-notch deepfake detection tools to protect themselves from evolving threats because defensive AI systems that identify generative AI threats represent an essential requirement.
Policy for Responsible Offensive AI Use
The development of appropriate policies for offensive AI usage remains essential to control AI cyber warfare threats effectively. The ongoing debate about ethical standards requires a solution to achieve proper AI cybersecurity benefits without enabling its misuse as shown in the image[cited].
Conclusion
Future of AI vs AI in Cybersecurity
The quick development of cybersecurity demands our knowledge about how generative AIs operate as security tools and dangerous threats. The dual nature of generative AI emerges when hackers employ it to develop complex phishing attacks.
Hackers employ ChatGPT and other tools to generate deceptive code which enhances their social engineering techniques.
The defensive AI technology sector shows promise for detecting AI-generated threats through its development of new deepfake detection systems.
The future direction of cybersecurity depends on our ability to establish ethical guidelines for penetration testing and offensive AI operations while developing responsible rules for their use.
Achieving proper equilibrium between AI technology usage in cyber warfare risk reduction requires us to create strategic plans which will help us stay ahead of emerging threats.
The digital security landscape undergoes complete transformation because AI tools revolutionize both offensive and defensive operations.
The image demonstrates the thin line between beneficial and detrimental aspects which requires your attention when discussing generative AI applications in cybersecurity.
Image2. The Dual Nature of AI in Cybersecurity
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