Runtime User Intent Detection for Insider Threat Access Control
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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 uses RASP analysis, UEBA, and CMFA to monitor user behavior within applications, detect anomalies, and block access or modify data resources based on malicious intent, employing machine learning models and continuous multi-factor authentication to confirm user identity and intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication systems are used to verify authorized users, then access control is established, but malicious intent by authorized users cannot be detected
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 applications, the system proactively identifies deviations from normal behavior that indicate potential malicious intent, allowing prevention before data breaches or damage occur.
Solution Approach 2:
The system implements continuous feedback loops where user behavior is constantly monitored, analyzed, and used to update threat assessments. The runtime security engine receives feedback from behavior analytics about user actions, adjusts threat levels dynamically, and modifies access decisions in real-time based on this feedback, creating a self-improving security system.
2Reliability
If runtime security analysis is implemented to detect malicious intent, then authorized users with malicious intent can be identified, but system performance may be impacted
Solution Approach 1:
The runtime security engine performs self-service by automatically collecting behavior data, analyzing patterns, and making security decisions without requiring external intervention. The system autonomously monitors user behavior, compares it against established profiles, identifies anomalies indicating malicious intent, and enforces security policies, reducing overhead and improving performance.
Solution Approach 2:
The system merges security analysis functions directly into the application runtime environment, combining behavior analytics, threat assessment, and access control into a unified runtime security engine. This integration eliminates the need for separate security infrastructure and allows security operations to occur alongside application execution without significant performance overhead.
3Reliability
If comprehensive user behavior monitoring is implemented, then malicious actions can be detected, but user privacy concerns increase
Solution Approach 1:
The system applies local quality by monitoring only the specific behavior patterns and data access patterns relevant to security threats, rather than collecting all possible user information. Behavior analytics focus on localized aspects of user interactions with applications, such as data access frequencies, timing patterns, and abnormal sequences of actions, minimizing privacy intrusion while maintaining detection accuracy.
4Reliability
If additional security analysis layers are added to detect malicious intent, then security coverage is improved, but device complexity increases
Solution Approach 1:
The runtime security engine performs multiple security functions universally - it conducts behavior analytics, performs threat assessment, makes access decisions, and enforces policies across all applications and users through a single integrated system. This multi-functional approach provides comprehensive security coverage without requiring separate specialized systems for each security function, reducing overall complexity.
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.