Open Access

Application of Artificial Intelligence in Digital Risk Protection and External Threat Intelligence

https://doi.org/10.55640/ijmcsit-v03i05-04

PDF

Kolchin Rustam ¹ ,

⁴ SoftLine PJSC Almaty, Kazakhstan

Abstract

Organizations now face external digital risks from look-alike domains, leaked credentials, dark web posts, impersonation profiles, and public technical traces that security teams cannot control. The article examines artificial intelligence in Digital Risk Protection and external threat intelligence as an analytical layer for collecting, classifying, prioritizing, and routing external risk signals. The study draws on recent academic publications, systematic reviews, industry guidance, and the MITRE ATT&CK Reconnaissance Framework. Source analysis, comparative analysis, conceptual synthesis, typological classification, and analytical generalization guide the research. The article distinguishes Digital Risk Protection from classical cyber threat intelligence. It explains the use of AI in domain similarity detection, homoglyph analysis, dark web monitoring, language-based extraction, and alert ranking. It replaces a linear workflow figure with a table that connects monitored sources, AI functions, and operational outputs. The proposed model suits enterprise security teams that need defensible prioritization before takedown, identity response, legal review, or SOC escalation.

Keywords

artificial intelligence, digital risk protection, external threat intelligence, dark web monitoring, phishing detection

References

Almuhaideb, A. M., Aslam, N., Alabdullatif, A., Altamimi, S., Alothman, S., Alhussain, A., Aldosari, W., Alsunaidi, S. J., & Alissa, K. A. (2022). Homoglyph attack detection model using machine learning and a hash function. Journal of Sensor and Actuator Networks, 11(3), Article 54. https://doi.org/10.3390/jsan11030054

Cascavilla, G., Tamburri, D. A., & Van Den Heuvel, W.-J. (2021). Cybercrime threat intelligence: A systematic multi-vocal literature review. Computers & Security, 105, Article 102258. https://doi.org/10.1016/j.cose.2021.102258

Chen, Y., Cui, M., Wang, D., Cao, Y., Yang, P., Jiang, B., Lu, Z., & Liu, B. (2024). A survey of large language models for cyber threat detection. Computers & Security, 145, Article 104016. https://doi.org/10.1016/j.cose.2024.104016

Dalvi, A., & Bhirud, S. (2024). Dark web monitoring as an emerging cybersecurity strategy for businesses. International Journal of Information Engineering and Electronic Business, 16(2), 54-67. https://doi.org/10.5815/ijieeb.2024.02.05

European Data Protection Board. (2025). AI privacy risks & mitigations: Large language models. https://www.edpb.europa.eu/our-work-tools/our-documents/support-pool-experts-projects/ai-privacy-risks-mitigations-large_en

Haq, Q. E. U., Faheem, M. H., & Ahmad, I. (2024). Detecting phishing URLs based on a deep learning approach to prevent cyber-attacks. Applied Sciences, 14(22), Article 10086. https://doi.org/10.3390/app142210086

Jaffal, N. O., Alkhanafseh, M., & Mohaisen, D. (2025). Large language models in cybersecurity: A survey of applications, vulnerabilities, and defense techniques. AI, 6(9), Article 216. https://doi.org/10.3390/ai6090216

MITRE. (2025). Reconnaissance, tactic TA0043: Enterprise. MITRE ATT&CK. https://attack.mitre.org/tactics/TA0043/

Nunez, J., Contu, R., & Schneider, M. (2024). Market guide for security threat intelligence products and services (ID G00794923). Gartner.

Zieni, R., Massari, L., & Calzarossa, M. C. (2023). Phishing or not phishing? A survey on the detection of phishing websites. IEEE Access, 11, 18499-18519. https://doi.org/10.1109/ACCESS.2023.3247135

International Journal of Modern Computer Science and IT Innovations

Application of Artificial Intelligence in Digital Risk Protection and External Threat Intelligence

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles