LLMs in Cyber Security: Bridging Practice and Education
Large Language Models (LLMs) have emerged as powerful tools in cyber security, enabling automation, threat detection, and adaptive learning. Their ability to process unstructured data and generate context-aware outputs supports both operational tasks and educational initiatives. Despite their growin...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/9/7/184 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850078250270720000 |
|---|---|
| author | Hany F. Atlam |
| author_facet | Hany F. Atlam |
| author_sort | Hany F. Atlam |
| collection | DOAJ |
| description | Large Language Models (LLMs) have emerged as powerful tools in cyber security, enabling automation, threat detection, and adaptive learning. Their ability to process unstructured data and generate context-aware outputs supports both operational tasks and educational initiatives. Despite their growing adoption, current research often focuses on isolated applications, lacking a systematic understanding of how LLMs align with domain-specific requirements and pedagogical effectiveness. This highlights a pressing need for comprehensive evaluations that address the challenges of integration, generalization, and ethical deployment in both operational and educational cyber security environments. Therefore, this paper provides a comprehensive and State-of-the-Art review of the significant role of LLMs in cyber security, addressing both operational and educational dimensions. It introduces a holistic framework that categorizes LLM applications into six key cyber security domains, examining each in depth to demonstrate their impact on automation, context-aware reasoning, and adaptability to emerging threats. The paper highlights the potential of LLMs to enhance operational performance and educational effectiveness while also exploring emerging technical, ethical, and security challenges. The paper also uniquely addresses the underexamined area of LLMs in cyber security education by reviewing recent studies and illustrating how these models support personalized learning, hands-on training, and awareness initiatives. The key findings reveal that while LLMs offer significant potential in automating tasks and enabling personalized learning, challenges remain in model generalization, ethical deployment, and production readiness. Finally, the paper discusses open issues and future research directions for the application of LLMs in both operational and educational contexts. This paper serves as a valuable reference for researchers, educators, and practitioners aiming to develop intelligent, adaptive, scalable, and ethically responsible LLM-based cyber security solutions. |
| format | Article |
| id | doaj-art-32b95ee59da84e49b1910e61ba6f393a |
| institution | DOAJ |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-32b95ee59da84e49b1910e61ba6f393a2025-08-20T02:45:37ZengMDPI AGBig Data and Cognitive Computing2504-22892025-07-019718410.3390/bdcc9070184LLMs in Cyber Security: Bridging Practice and EducationHany F. Atlam0Cyber Security Centre, WMG, University of Warwick, Coventry CV4 7AL, UKLarge Language Models (LLMs) have emerged as powerful tools in cyber security, enabling automation, threat detection, and adaptive learning. Their ability to process unstructured data and generate context-aware outputs supports both operational tasks and educational initiatives. Despite their growing adoption, current research often focuses on isolated applications, lacking a systematic understanding of how LLMs align with domain-specific requirements and pedagogical effectiveness. This highlights a pressing need for comprehensive evaluations that address the challenges of integration, generalization, and ethical deployment in both operational and educational cyber security environments. Therefore, this paper provides a comprehensive and State-of-the-Art review of the significant role of LLMs in cyber security, addressing both operational and educational dimensions. It introduces a holistic framework that categorizes LLM applications into six key cyber security domains, examining each in depth to demonstrate their impact on automation, context-aware reasoning, and adaptability to emerging threats. The paper highlights the potential of LLMs to enhance operational performance and educational effectiveness while also exploring emerging technical, ethical, and security challenges. The paper also uniquely addresses the underexamined area of LLMs in cyber security education by reviewing recent studies and illustrating how these models support personalized learning, hands-on training, and awareness initiatives. The key findings reveal that while LLMs offer significant potential in automating tasks and enabling personalized learning, challenges remain in model generalization, ethical deployment, and production readiness. Finally, the paper discusses open issues and future research directions for the application of LLMs in both operational and educational contexts. This paper serves as a valuable reference for researchers, educators, and practitioners aiming to develop intelligent, adaptive, scalable, and ethically responsible LLM-based cyber security solutions.https://www.mdpi.com/2504-2289/9/7/184Large Language Models (LLMs)cyber securitycyber security educationgenerative AIprivacyethics |
| spellingShingle | Hany F. Atlam LLMs in Cyber Security: Bridging Practice and Education Big Data and Cognitive Computing Large Language Models (LLMs) cyber security cyber security education generative AI privacy ethics |
| title | LLMs in Cyber Security: Bridging Practice and Education |
| title_full | LLMs in Cyber Security: Bridging Practice and Education |
| title_fullStr | LLMs in Cyber Security: Bridging Practice and Education |
| title_full_unstemmed | LLMs in Cyber Security: Bridging Practice and Education |
| title_short | LLMs in Cyber Security: Bridging Practice and Education |
| title_sort | llms in cyber security bridging practice and education |
| topic | Large Language Models (LLMs) cyber security cyber security education generative AI privacy ethics |
| url | https://www.mdpi.com/2504-2289/9/7/184 |
| work_keys_str_mv | AT hanyfatlam llmsincybersecuritybridgingpracticeandeducation |