Advancing Identity and Access Management with Artificial Intelligence for Anomaly Detection : A proof of concept implementation study

Sammanfattning

This degree project explores the application of AI-driven anomaly detection within Identity and Access Management (IAM), focusing on the Serix IAM system. The project investigates the use of the Isolation Forest algorithm to detect anomalies in access rights per individual, providing a proof of concept implementation. The proof of concept integrates directly with the IAM system database and implements an Application Programming Interface (API) to trigger the anomaly detection process from the IAM system backend. The IAM system frontend implements a user interface for the configuration of algorithm parameters and for reviewing detected anomalies. The results demonstrate the feasibility of implementing AI-driven anomaly detection, highlighting its time-saving potential and limitations in contextualizing detected anomalies.