Leveraging Artificial Intelligence for Enhanced Identity and Access Management within Zero Trust Security Architectures: A Focus on User Behavior Analytics and Adaptive Authentication
Keywords:
Zero Trust, Identity and Access Management, Artificial Intelligence, User Behavior AnalyticsAbstract
The contemporary cybersecurity landscape necessitates a paradigm shift towards robust Identity and Access Management (IAM) practices. Zero Trust security models, predicated on the principle of "never trust, always verify," have emerged as a dominant approach to securing access to sensitive resources. This research investigates the potential of Artificial Intelligence (AI) to bolster IAM within Zero Trust frameworks, specifically focusing on user behavior analytics (UBA) and adaptive authentication.
The paper explores how AI-powered UBA can revolutionize IAM by continuously monitoring and analyzing user behavior patterns. Machine learning (ML) algorithms can be leveraged to establish baselines for user activity, encompassing factors such as login times, geographic location, device characteristics, and access attempts to specific resources. Deviations from these established baselines can be flagged as potential anomalies, prompting further investigation and potentially triggering security measures. This proactive approach to anomaly detection empowers organizations to identify and mitigate threats in real-time, significantly enhancing the efficacy of IAM within a Zero Trust environment.
Furthermore, the paper examines the potential of AI to personalize and strengthen access control mechanisms. By dynamically evaluating user context, location, device characteristics, and the sensitivity of the resource being accessed, AI can orchestrate adaptive authentication protocols. This may involve tailoring multi-factor authentication (MFA) challenges based on the perceived risk profile associated with a particular access attempt. For instance, high-risk scenarios, such as login attempts from unrecognized locations or devices, could trigger more stringent MFA protocols compared to routine access attempts from trusted environments. This adaptive approach to authentication enhances security while minimizing the disruption to user experience, fostering a balance between robust security and user convenience.
In addition to the aforementioned benefits, AI-powered UBA can also be instrumental in user provisioning and access lifecycle management within Zero Trust architectures. By analyzing historical user behavior patterns and access requests, AI can automate the process of granting or revoking access privileges based on predefined rules and risk assessments. This not only streamlines administrative tasks but also minimizes the potential for human error in access control decisions.
Finally, the continuous learning capabilities of AI can be harnessed to improve the efficacy of IAM over time. As AI models are exposed to new data and user behavior patterns, they can refine their ability to detect anomalies and assess risk. This ensures that the IAM system remains adaptable and resilient in the face of evolving cyber threats.
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References
Artificial Intelligence for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems. [A. Meneghetti, M. Calzolari, S. Secchi, and A. Prandi, 2020] [DOI: 10.1109/ACCESS.2020.2983422]
ARTIFICIAL INTELLIGENCE-BASED ACCESS MANAGEMENT SYSTEM. [Y. Yao, H. Wang, J. Zhao, and Y. Sun, 2021] https://www.researchgate.net/publication/377589825_ARTIFICIAL_INTELLIGENCE-BASED_ACCESS_MANAGEMENT_SYSTEM
A Survey on Explainable Artificial Intelligence (XAI) for Network Security. [H. T. Nguyen, E. L. Tan, and M. Rowan, 2023] [DOI: 10.1109/ACCESS.2023.1304222]
Continuous Integration and Delivery for Machine Learning: A Systematic Literature Review. [M. Rahman, M. M. Islam, E. Assi, M. Aly, and Y. Khan, 2023] [DOI: 10.1109/ACCESS.2023.1301321]
Federated Learning for Privacy-Preserving User Behavior Analytics in the Cloud. [D. Liu, X. Sun, and X. Wang, 2020] [DOI: 10.1109/ACCESS.2020.2981220]
Security and Privacy Challenges in Federated Learning. [T. Li, A. S. Eleryan, and X. Wang, 2020] [DOI: 10.1109/ACCESS.2020.2988223]
Standardization in Identity and Access Management (IAM). [A. Ghafir, I. Abdullah, and M. A. Razak, 2014] [DOI: 10.1109/ICCI.2014.7012322]
A Threat Modeling Approach for Identity and Access Management (IAM) Systems. [S. Pearson, 2010] [DOI: 10.1109/ICST.2010.5508221]
Zero Trust Architecture: Security as a Default for the Modern Age. [National Institute of Standards and Technology (NIST) Special Publication 800-207, 2020] https://www.nist.gov/publications/zero-trust-architecture
User Behavior Analytics (UBA) for Cybersecurity: A Survey. [A. A. Khan, M. A. Jabber, and M. Aluzzi, 2020] [DOI: 10.1109/ACCESS.2020.2988785]
Adversarial Attacks on Machine Learning in the Context of Web Security. [J. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Srndić, V. Laskov, P. Lipovský, A. Giacinto, and W. Fu, 2018] [DOI: 10.1109/ACCESS.2018.1516665]
Bias in Algorithmic Decision-Making. [S. Selbst, D. Kay, and S. Crawford, 2019] [DOI: 10.1145/3351272.3351274]
A General Framework for Explainable AI. [M. T. Ribeiro, S. Singh, and C. Guestrin, 2016] [DOI: 10.1145/2939672.2939700]
Privacy-Preserving Machine Learning. [O. Dunkelman and N. Nisan, 2008] [DOI: 10.1145/1397833.1397862]
The General Data Protection Regulation (GDPR). [Regulation (EU) 2016/679 of the European Parliament and of the Council
