Application Security Intent Detection for Insider Threat Blocking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing authentication systems fail to identify and mitigate security threats posed by authorized users with malicious intent, leading to potential data breaches and unauthorized access to sensitive information.
Innovation Solution
Implementing a security system that utilizes RASP analysis, UEBA, and continuous multi-factor authentication to monitor user behavior within applications, detect anomalies, and block access or modify data resources based on malicious intent, employing machine learning models and authentication factors to confirm user identity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication systems are used to verify authorized users, then user access is granted based on identity verification, but authorized users with malicious intent can still access and manipulate sensitive data resources
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and establishes baseline profiles before malicious activities occur. By continuously monitoring and analyzing user interactions with data resources, the system proactively identifies deviations from normal behavior patterns, enabling early detection and prevention of insider threats before significant damage can be done.
Solution Approach 2:
The patent introduces an intermediary security system that sits between the authorized user and the data resources. This intermediary layer analyzes user requests in real-time, evaluating behavior patterns, authentication factors, and contextual information to determine whether to allow or block access, thereby preventing malicious actions while maintaining legitimate user productivity.
2Ease of operation
If access control is based solely on user authentication, then authorized users can access all resources within their permission scope, but this approach cannot distinguish between malicious and non-malicious behavior
Solution Approach 1:
The system transitions from static access control based on fixed permissions to dynamic access control that adapts in real-time based on user behavior analysis. Access decisions are continuously adjusted according to the user's current activity patterns, contextual factors, and deviation from established baselines, allowing the system to maintain ease of operation for legitimate users while blocking malicious actions.
Solution Approach 2:
The patent changes the parameters used for access control from simple identity verification to multiple dynamic parameters including behavior patterns, authentication factors, contextual information, and risk scores. By analyzing changes in these parameters over time, the system can distinguish between normal variations in user behavior and malicious activities, maintaining operational convenience while enhancing security.
3Measurement precision
If comprehensive user behavior monitoring is implemented to detect malicious intent, then security detection capability is improved, but system complexity and computational overhead increase
Solution Approach 1:
The security system is segmented into modular components that can be independently deployed and managed. Behavior analysis functions, authentication verification, risk assessment, and access control decisions are separated into distinct modules, allowing the system to scale complexity according to needs and reducing the burden of implementing comprehensive monitoring across the entire system at once.
Solution Approach 2:
The system implements partial monitoring focused on high-risk operations and critical data resources rather than attempting to monitor all user activities equally. By concentrating analytical resources on behaviors that pose the greatest security risk, the system achieves high detection accuracy for malicious intent while minimizing overall system complexity and computational overhead.
Data Source
AI summary
An example method identifying a request to access or modify a data resource. The request is made by a user. The example method further includes authenticating the user. Based on authenticating the user, the example method includes determining that the request is associated with a malicious intent based on a characteristic of the user. Further, based on determining that the request is associated with the malicious intent, the example method includes blocking the user from accessing or modifying the data resource.