Augmenting cloud identity security: AI-assisted threat modeling for enhanced vulnerability detection
Year: 2025
Type: article
Abstract: This article examines the emerging integration of artificial intelligence with traditional threat modeling approaches for cloud-based identity systems. As organizations increasingly migrate identity infrastructure to cloud environments, security professionals face unprecedented complexity in identifying and mitigating potential vulnerabilities. The article explores how AI-assisted threat modeling can enhance the detection of sophisticated attack vectors while addressing the ethical implications of automated security analysis. Through examination of implementation cases across financial services, healthcare, and public sector applications, the article identifies patterns of successful human-AI collaboration in security contexts. Particular attention is given to regulatory compliance requirements and the mitigation of algorithmic bias in security decision-making processes. The article demonstrates that AI-augmented threat modeling, when implemented with appropriate ethical guardrails, offers significant advantages in scenario simulation, pattern recognition, and predictive analysis compared to conventional methods. This article contributes to the evolving discourse on responsible AI deployment in critical security infrastructure and provides a framework for security practitioners to effectively leverage AI capabilities while maintaining human oversight.
Author: Rajat Kumar Gupta
Institution: Indian Institute of Technology Guwahati
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409832051