The Role of AI in Cloud-Based Identity and Access Management (IAM) for Enterprise Security
Keywords:
AI-driven IAM, cloud security, enterprise cybersecurityAbstract
Artificial intelligence has turn out as an evolutionary force in cloud-based Identity and Access Management (IAM) which enhances security by improving authentication protocols, automating threat detection, and reinforcing zero-trust security models. As companies rapidly adapting cloud environment AI-based IAM provides dynamic access control, continuous authentication mechanisms, and sophisticated anomaly detection techniques that significantly reduces insider threats and unauthorised access. The objective of this study is to examine AI powered IAM implementation in high-risk industries based on Microsoft Azure Active Directory (Azure AD) and Amazon Web Services Identity and Access Management (AWS IAM).
Downloads
References
N. Alhadawi, R. G. Bellekens, and K. Kifayat, "Artificial intelligence-driven identity
and access management: A zero-trust approach," IEEE Access, vol. 10, pp.
–45247, 2022.
J. Kim and H. Lee, "AI-driven anomaly detection for identity and access management
in cloud computing," IEEE Transactions on Information Forensics and Security, vol.
, pp. 2017–2031, 2023.
Singu, Santosh Kumar. "Real-Time Data Integration: Tools, Techniques, and Best
Practices." ESP Journal of Engineering & Technology Advancements 1.1 (2021):
-172.
S. Kumari, "Agile Cloud Transformation in Enterprise Systems: Integrating AI for
Continuous Improvement, Risk Management, and Scalability", Australian Journal of
S. Kumari, "AI-Enhanced Agile Development for Digital Product Management:
Leveraging Data-Driven Insights for Iterative Improvement and Market Adaptation",
Adv. in Deep Learning Techniques, vol. 2, no. 1, pp. 49-68, Mar. 2022
Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure
and Databricks." ESP Journal of Engineering & Technology Advancements 1.2
(2021): 176-187.
S. Kumari, "AI-Driven Cybersecurity in Agile Cloud Transformation: Leveraging
Machine Learning to Automate Threat Detection, Vulnerability Management, and
Incident Response", J. of Art. Int. Research, vol. 2, no. 1, pp. 286-305, Apr. 2022
X. Luo, P. Li, and J. Chen, "Quantum-resistant cryptography for AI-powered IAM
systems in cloud security," IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp.
–312, Feb. 2024.
A. Shukla, V. Gupta, and S. Bose, "Zero-knowledge proofs for AI-powered
decentralized identity management," IEEE Transactions on Information Forensics
and Security, vol. 18, pp. 567–582, Jan. 2023.
Y. Wang and M. Patel, "AI-driven IAM for high-risk industries: Case studies in finance
and healthcare," IEEE Transactions on Industrial Informatics, vol. 19, no. 3, pp.
–1302, Mar. 2023.
H. Zhang, R. Sharma, and T. Nguyen, "Cloud-native IAM: AI-powered identity
governance and administration," IEEE Transactions on Services Computing, vol. 16,
no. 2, pp. 235–248, Feb. 2023.
P. Wang, K. Chen, and M. Sun, "AI-powered threat intelligence in IAM: A zero-trust
security perspective," IEEE Transactions on Network and Service Management, vol.
, no. 4, pp. 654–669, Dec. 2023.
F. R. Miller and T. K. Cho, "Adversarial AI in identity management: Emerging threats
and countermeasures," IEEE Transactions on Artificial Intelligence, vol. 5, no. 1, pp.
–89, Jan. 2024.
K. A. Farooq and L. D. Hansen, "Scalability challenges in AI-driven IAM solutions: A
multi-cloud approach," IEEE Transactions on Parallel and Distributed Systems, vol.
, no. 2, pp. 390–404, Feb. 2024.
A. S. Patel and M. J. Kim, "Neural networks for fraud detection in AI-powered identity
access management," IEEE Transactions on Neural Networks and Learning
Systems, vol. 34, no. 1, pp. 92–105, Jan. 2024.
S. Das, Y. Xie, and J. Y. Lin, "AI-driven IAM in hybrid cloud environments: A
blockchain-based approach," IEEE Transactions on Cloud Computing, vol. 12, no. 1,
pp. 121–136, Jan. 2024.
