AI Driven Identity Intelligence for Enterprise IAM: Predictive Risk Analytics and Automated Governance in Financial Cloud Environments
DOI:
https://doi.org/10.15680/IJCTECE.2025.0801013Keywords:
Artificial Intelligence, IAM, Predictive Risk Analytics, Anomaly Detection, Cloud Security, Behavioral AnalysisAbstract
The paper suggests an AI-based Identity Intelligence architecture of an enterprise IAM in financial clouds. The system is a combination of behavioral analysis, risk scoring, anomaly detection, and automated governance to enhance identity security. The experimental results demonstrate a huge performance improvement compared to traditional IAM systems, with an increase in detection accuracy (78% to 94%), precision (75% to 92%) and recall (70% to 93%). The rate of false positives drops from 18% to 6% and the response time decreases from 2.8s to 0.9s. It also improves automation with 89% access review automation and 95% efficiency in compliance as well as proactive and adaptive identity governance
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