Autonomous, AI-based security monitoring system (100) for Identity & Access Management (IAM) gateways

Abstract

An autonomous, AI-based security monitoring system (100) for identity and access management gateways, wherein the system comprises: an IAM gateway module that includes an authentication engine configured to receive and validate user credentials using a variety of authentication protocols; an authorization engine that is functionally coupled with the authentication engine and configured to evaluate access control policies based on authenticated user identities using a hybrid authorization model that combines role-based, attribute-based, and policy-based access control; and a session management unit configured to create, manage, and terminate user sessions based on authorization decisions;an AI-based anomaly detection engine, functionally coupled with the IAM gateway module, and including a behavioral analytics module that implements unattended clustering algorithms to create and continuously update multidimensional baselines of user behavior; a machine learning model repository that stores trained deep neural network models for temporal and contextual analysis of authentication events; and a real-time threat classification unit configured to categorize detected anomalies into threat categories with dynamic risk assessments;a security event correlation module that includes a log aggregation unit configured to collect security event logs from the IAM gateway module, the anomaly detection engine, and external security sources and normalize them into a unified event schema, and an event pattern analyzer that implements a graph-based correlation engine to identify multi-stage attack chains through temporal and causal relationship analysis;an autonomous response engine comprising a policy enforcement unit configured to assess threat classifications against response policies and issue enforcement instructions, an adaptive access control unit operationally coupled with the authorization engine and configured to dynamically modify access control policies based on dynamic risk assessments, and an incident remediation module configured to execute automated workflows to resolve incidents;characterized in that the AI-based anomaly detection engine is directly embedded in the architecture of the IAM gateway module to analyze authentication and authorization event streams in real time without dependence on external systems, and the autonomous response engine performs adaptive countermeasures at the gateway level, including enforcing enhanced authentication, revoking privileges, and terminating sessions, autonomously within a response cycle of less than two seconds, and wherein the incident resolution module implements a feedback loop with the behavioral analysis module to continuously refine behavioral baselines based on incident outcomes.