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Journals / Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi / 2026 / Cilt: 7 Sayı: 2

An Analysis of Cybersecurity News in the Age of Generative Artificial Intelligence

Üretken Yapay Zekâ Çağında Siber Güvenlik Haberleri Üzerine Analiz Çalışması

Esra Metin0009-0002-8192-2762
Pages
340–354
Publication Date
2026-10-22
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings indicate that, with the advancement of generative AI, cyberattacks that previously required specialized technical expertise have become easier to execute and are occurring more frequently through the use of these AI models.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With the widespread adoption of computer networks and the Internet, cybersecurity has become an increasingly important issue.
📊
Key Findings & Data:The analysis of the news headlines revealed that deepfake-related news accounted for 13.6% of the dataset, while phishing-related news represented 4.6%.
💡
Conclusion & Impact:These findings indicate that, with the advancement of generative AI, cyberattacks that previously required specialized technical expertise have become easier to execute and are occurring more frequently through the use of these AI models.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Deepfake · Siber Güvenlik · Jailbreak

Abstract

With the widespread adoption of computer networks and the Internet, cybersecurity has become an increasingly important issue. The rapid advancement of generative artificial intelligence (AI) technologies, particularly Large Language Models (LLMs) and deep generative models such as Generative Adversarial Networks (GANs) and diffusion models, has fundamentally transformed both defensive and offensive capabilities in cybersecurity. This study analyzes cybersecurity news headlines to understand the impact of generative AI on phishing and social engineering attacks, deepfakes and voice cloning, malware generation through large language models, jailbreak attacks designed to bypass security restrictions, and cyber threat intelligence. The analysis of the news headlines revealed that deepfake-related news accounted for 13.6% of the dataset, while phishing-related news represented 4.6%. These findings indicate that, with the advancement of generative AI, cyberattacks that previously required specialized technical expertise have become easier to execute and are occurring more frequently through the use of these AI models.

Özet

Bilgisayar ağlarının ve internetin yaygınlaşmasıyla birlikte, siber güvenlik konusu daha önemli hale gelmiştir. Üretken yapay zekâ teknolojilerinin, özellikle büyük dil modellerinin (LLM) ve derin üretici ağların (GAN, difüzyon modelleri) hızla yaygınlaşması, siber güvenlik alanında hem savunma hem de saldırı kapasitelerini kökten dönüştürmüştür. Bu çalışma, üretken yapay zekânın kimlik avı ve sosyal mühendislik, derin sahtecilik (deepfake) ve ses klonlama, büyük dil modelleri üzerinden kötü amaçlı yazılım üretimi ve güvenlik kısıtlarını aşma (jailbreak) saldırıları ile siber tehdit istihbaratı alanlarındaki etkilerini anlamak için haber başlıklarının analizleri yapılmıştır. İncelenen haber başlıkları, Deepfake %13,6, kimlik avı (oltalama) %4,6’lık bir orana sahip olduğunu göstermiştir. Bu sonuç, üretken yapay zekânın gelişmesiyle beraber özellikle uzman bilgisi gerektiren saldırıların bu modellerin kullanılmasıyla daha basit ve sıklaştığını göstermektedir.

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Cite This Article

Metin, E. (2026). An Analysis of Cybersecurity News in the Age of Generative Artificial Intelligence. Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi, Cilt: 7 Sayı: 2, 340–354. https://ajindex.com/articles/1595785

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References (34)

  1. Akbar, R., & Alharbi, S. A. (2026). Future-Ready Cyber Defense: AI-Based Identity Management Against Phishing Attacks. 1-7. https://doi.org/10.13140/RG.2.2.27320.61447
  2. Akhtar, Z. B. (2024). Unveiling the evolution of generative AI (GAI): A comprehensive and investigative analysis toward LLM models (2021-2024) and beyond. Journal of Electrical Systems and Inf Technol, 11:22, 1-21. https://doi.org/10.1186/s43067-024-00145-1
  3. Alanazi, S., & Asif, S. (2024). Exploring deepfake technology: creation, consequences and countermeasures. Hum.-Intell. Syst. Integr. 6, 49–60. https://doi.org/10.1007/s42454-024-00054-8
  4. Alrabea, K. J., Alsaffar, M., Alsafran, M. A., Alsaber, A., Almutairi, S., Al-Saeed, F., & Alkandari, A. M. (2026). Artificial intelligence and cybersecurity within a social media context: implications and insights for Kuwait. Journal of Science and Technology Policy Management, 17(1), 6–24. https://doi.org/10.1108/JSTPM-12-2023-0224
  5. Amezaga, N., & Hajek, J. (2022). Availability of Voice Deepfake Technology and its Impact for Good and Evil. SIGITE '22: Proceedings of the 23rd Annual Conference on Information Technology Education, 23–28. https://doi.org/10.1145/3537674.3554742
  6. Apruzzese, G., Laskov, P., Montes de Oca, E., Mallouli, W., Rapa, L. B., Grammatopoulos, A. V., & Di Franco, F. (2023). The Role of Machine Learning in Cybersecurity. Digital Threats, 4(1), Article 8, 38. https://doi.org/10.1145/3545574
  7. Arslan, F. (2023). Deepfake Technology: A Criminological Literature Review. Sakarya Üniversitesi Hukuk Fakültesi Dergisi, 11(1), 701-20. https://doi.org/10.56701/shd.1293642
  8. Chu, J., Liu, Y., Yang, Z., Shen, X., Backes, M., & Zhang, Y. (2024). Comprehensive Assessment of Jailbreak Attacks Against LLMs. CISPA, 1-25. https://doi.org/10.60882/cispa.32775177.v1
  9. Dandotiya, M., Goyal, N. K., Khunteta, A., & Tiwari, B. (2026). Real time identification of phishing attacks through machine learning enhanced browser extensions. Sci Rep, 16, 6612, 1-25. https://doi.org/10.1038/s41598-026-35655-7
  10. Florackis, C., Louca, C., Michaely, R., & Weber, M. (2023). Cybersecurity Risk. The Review of Financial Studies, 36(1), 351–407. https://doi.org/10.1093/rfs/hhac024
  11. Helmus, T. C. (2022). Artificial Intelligence, Deepfakes, and Disinformation: A Primer. RAND CORP, 1-24. https://doi.org/10.7249/PEA1043-1
  12. Hossain, I., Ahad, T., Alam, M. D. J., Puppala, S., Alam, S. B., & Talukder, S. (2026). The Art of the Jailbreak: Formulating Jailbreak Attacks for LLM Security Beyond Binary Scoring. 1-36. https://doi.org/10.48550/arXiv.2605.09225
  13. Kartik, A., Chanelle, D., & Prasanna, G. (2026). Cybersecurity and financial stability. Review of Finance, 30(3), 1109–1150. https://doi.org/10.1093/rof/rfaf079
  14. Kaswan, K. S., Dhatterwal, J. S., Malik, K., & Baliyan, A. (2023). Generative AI: A Review on Models and Applications. 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI), Greater Noida, India, 699-704. https://doi.org/10.1109/ICCSAI59793.2023.10421601
  15. Kenthapadi, K., Lakkaraju, H., & Rajani, N. (2023). Generative AI Meets Responsible AI: Practical Challenges and Opportunities. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’23), August 6–10, 2023, Long Beach, CA, USA. ACM New York, NY, USA, 5805–5806. https://doi.org/10.1145/3580305.3599557
  16. Kincl, J., Adam, M. T. P., & Pavleska, T. (2026). Human-AI Integration in Cybersecurity: An Industry-Aligned Perspective on Incident Management. Int. J. Inf. Secur., 25(103), 1-29. https://doi.org/10.1007/s10207-026-01259-5
  17. Mahmud, B. U., & Sharmin, A. (2020). Deep Insights of Deepfake Technology: A Review. DUJASE, 5(1), 13-23. https://doi.org/10.48550/arXiv.2105.00192
  18. Mubarak, R., Alsboui, T., Alshaikh, O., Inuwa-Dutse, I., Khan, S., & Parkinson, S. (2023). A Survey on the Detection and Impacts of Deepfakes in Visual, Audio, and Textual Formats. IEEE Access, 1, 144497-144529. https://doi.org/10.1109/ACCESS.2023.3344653
  19. Mustak, M., Salminen, J., Mäntymäki, M., Rahman, A., & Dwivedi, Y. K. (2023). Deepfakes: Deceptions, mitigations, and opportunities. Journal of Business Research, 154, 113368, 1-15. https://doi.org/10.1016/j.jbusres.2022.113368
  20. Özgür, R., & Eren, T. (2026). Strategy Selection Against Cyber and Phishing Attacks in the Financial Sector Using MCDM Methods. Bilişim Teknolojileri Dergisi, 19(2), 185-199. https://doi.org/10.17671/gazibtd.1724633
  21. Panteli, N., Nthubu, B. R., & Mersinas, K. (2026). Being Responsible in Cybersecurity: A Multi-Layered Perspective. Inf Syst Front, 28, 209–227. https://doi.org/10.1007/s10796-025-10588-0
  22. Partila, P., Tovarek, J., Ilk, G. H., Rozhon, J., & Voznak, M. (2020). Deep Learning Serves Voice Cloning: How Vulnerable Are Automatic Speaker Verification Systems to Spoofing Trials? IEEE Communications Magazine, 58(2), 100-105. https://doi.org/10.1109/MCOM.001.1900396
  23. Peng, D., Jun, K., Dan, M., Xuezhi, C., Yunsen, X., Jiajun, C., & Shujian, H. (2024). A Wolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2136–2153, Mexico City, Mexico. https://doi.org/10.18653/v1/2024.naacl-long.118
  24. Rani, G., Singh, J., & Khanna, A. (2023). Comparative Analysis of Generative AI Models. 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), Faridabad, India, 760-765. https://doi.org/10.1109/ICAICCIT60255.2023.10465941
  25. Rao, P. V., Thimothi, V., & Harika, K. (2026). AI-driven Phishing Detection Utilizing Natural Language Processing Techniques. 2026 International Conference on Intelligent Computing, Networks, and Security (IC-ICNS), Bhubaneswar, India, 1-7. https://doi.org/10.1109/IC-ICNS68863.2026.11537691
  26. Sajid, S., Fazil, A. W., & Hakimi, M. (2026). AI-based Phishing Attacks on University Networks: A Systematic Literature Review and Defense Framework. Journal of Advanced Computer Knowledge and Algorithms, 3(2), 62-75. https://doi.org/10.29103/jacka.v3i2.26650
  27. Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. J Big Data, 7, 41, 1-29. https://doi.org/10.1186/s40537-020-00318-5
  28. Savaş, S., & Karataş, S. (2022). Cyber governance studies in ensuring cybersecurity: an overview of cybersecurity governance. Int. Cybersecur. Law Rev., 3, 7–34. https://doi.org/10.1365/s43439-021-00045-4
  29. Ullah, F., Ye, X., Fatima, U., Wu, Y., Akhtar, Z., & Ahmad H. (2026). What skills do cybersecurity professionals need? Information and Computer Security, 1-19. https://doi.org/10.1108/ICS-01-2025-0027
  30. Wang, H., Du, J., Yan, T., Cheng, Z., & Zhu, C. (2025). Review on Development of Deep Learning-Based Voice Cloning Technology. 2025 International Conference on Artificial Intelligence, Human-Computer Interaction and Natural Language Processing (ICAHN), Ningbo, China, 29-34. https://doi.org/10.1109/ICAHN67688.2025.00013
  31. Westerlund, M. (2019). The Emergence of Deepfake Technology: A Review. Technology Innovation Management Review, 9(11), 40-53. http://doi.org/10.22215/timreview/1282
  32. Yigit, Y., Buchanan, W. J., Tehrani, M. G., & Maglaras, L. (2025). Review of Generative AI methods in cybersecurity. Internet of Things and Cyber-Physical Systems, 5, 241-261. https://doi.org/10.1016/j.iotcps.2026.04.001
  33. Younes, M. B. (2026). Towards Preparing a Representative Phishing Dataset. 2026 International Conference on Smart Multidomain Integrated Learning Environments (ICSMILE), Irbid, Jordan, 1-6. https://doi.org/10.1109/ICSMILE69273.2026.11519258
  34. Zhengchun, S., Wenlan, W., & Weiheng, B. (2026). Evolving Security in LLMs: A Study of Jailbreak Attacks and Defenses. Proc. of International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA 2026), 5-7 February 2026, Boracay-Philippines, 1-10. https://doi.org/10.48550/arXiv.2504.02080