IAM Session Monitoring for Real-Time Anomalous Action Detection
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Solution Overview
Problem
Existing systems fail to detect anomalous identity and access management (IAM) actions in real-time, leading to potential data integrity, security, and operational issues, requiring significant post-incident resource allocation to mitigate impacts.
Innovation Solution
A detection system that processes change orders to determine expected IAM actions, monitors IAM sessions, and identifies anomalous events, sending real-time notifications to mitigate impacts automatically or manually.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time monitoring of IAM sessions is implemented, then detection speed of anomalous actions is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system pre-establishes a baseline of normal IAM actions by analyzing historical data and change orders before monitoring begins. This preliminary characterization of expected behavior enables real-time detection without requiring complex analysis of every action from scratch, reducing computational overhead while maintaining detection speed
Solution Approach 2:
The system continuously compares monitored IAM actions against the established baseline and provides real-time feedback when deviations are detected. This feedback mechanism enables rapid detection of anomalous actions while allowing the system to adapt and refine its detection criteria over time, balancing detection accuracy with system complexity
2Measurement precision
If comprehensive monitoring of all IAM actions is performed, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts and monitors only the critical attributes and characteristics of IAM actions that are most indicative of anomalous behavior, rather than analyzing every detail of each action. This selective extraction of key features maintains detection accuracy while significantly reducing processing time and computational resource requirements
Solution Approach 2:
The system applies different monitoring intensities and analysis depths to different types of IAM actions based on their risk profiles. High-risk actions undergo comprehensive analysis while low-risk routine actions receive lighter monitoring, optimizing the balance between detection accuracy and processing efficiency across the entire IAM workflow
3Loss of time
If automated detection and notification systems are deployed, then response time to security incidents is improved, but implementation cost and system complexity increase
Solution Approach 1:
The system automatically performs detection, analysis, and notification functions without requiring manual intervention. It self-manages the entire security monitoring workflow from baseline establishment through anomaly detection to alert generation, reducing response time while keeping operational complexity manageable through automation rather than manual processes
Data Source
AI summary
A system for detecting anomalous identity and access management (IAM) actions obtains a change order that indicates one or more changes to be implemented in an environment. The system determines one or more expected IAM actions associated with the change order and identifies a user account associated with implementing the one or more changes in the environment. The system monitors an IAM session in the environment that is associated with the user account for an activity of the user account. The system determines, based on monitoring the IAM session and based on the one or more expected IAM actions, that an anomalous IAM action event has occurred. The system sends, to another device and based on determining that the anomalous IAM action event has occurred, a notification indicating that the anomalous IAM action event has occurred.