Endpoint Access Control With Continuous User Risk Scoring
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Solution Overview
Problem
Conventional zero trust access control systems fail to continuously reassess user access permissions over time, making them vulnerable to breaches from stolen identities, compromised credentials, and malicious insiders, as they rely on point-in-time authentication and do not effectively monitor user behavior.
Innovation Solution
Implementing a security agent on endpoint devices that uses a combination of security policies and trained machine learning models to continuously monitor and calculate an aggregated risk score, dynamically revoking or modifying access permissions based on real-time user behavior analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If point-in-time authentication is used to verify user identity, then authentication speed is improved, but security reliability deteriorates because the system cannot detect compromised credentials or stolen identities after initial access is granted
Solution Approach 1:
The system implements continuous monitoring of user behavior and periodic recalculation of risk scores throughout the user session, rather than performing authentication only at the beginning. This continuous assessment maintains security reliability while allowing legitimate users to continue their work without interruption.
Solution Approach 2:
The system uses machine learning models to analyze user behavior patterns and provides feedback by adjusting risk scores in real-time based on detected anomalies. This feedback mechanism allows the system to respond dynamically to potential security threats while maintaining fast authentication for legitimate users.
2Reliability
If continuous monitoring of user behavior is implemented to reassess access permissions, then security reliability is improved, but system complexity increases due to the need for continuous risk score calculation and behavior analysis
Solution Approach 1:
The system employs machine learning models that automatically learn and adapt to individual user behavior patterns without requiring manual configuration or intervention. The models self-adjust to normal behavior variations and automatically detect anomalies, reducing the operational complexity of continuous monitoring.
Solution Approach 2:
The system dynamically adjusts risk score thresholds and monitoring intensity based on the user's risk profile and behavior patterns. Low-risk users experience minimal disruption with lower monitoring intensity, while high-risk users trigger more intensive scrutiny, optimizing the balance between security and system complexity.
3Measurement precision
If dynamic risk score calculation using machine learning models is performed continuously, then measurement precision of user risk assessment is improved, but computational energy consumption increases
Solution Approach 1:
The system performs risk score recalculation at periodic intervals rather than continuously, and adjusts the frequency based on user risk profiles. Low-risk users are assessed less frequently, reducing computational energy consumption, while high-risk users trigger more frequent assessments to maintain measurement precision when needed.
Solution Approach 2:
The system applies full computational resources for risk assessment only when necessary - such as when anomalies are detected or for high-risk users - rather than uniformly applying maximum precision to all users at all times. This partial action approach maintains measurement precision for critical cases while reducing overall energy consumption.
4Reliability
If strict zero trust access control is applied to all users, then security reliability is improved, but ease of operation deteriorates because legitimate users may be blocked by false positives
Solution Approach 1:
The system applies different levels of security scrutiny to different users based on their individual risk profiles and behavior patterns. Low-risk users experience minimal friction with fast, seamless access, while high-risk users undergo more intensive verification. This local differentiation maintains security reliability while preserving ease of operation for legitimate users.
Solution Approach 2:
The system dynamically adjusts access control decisions based on real-time risk assessments rather than applying static, uniform rules to all users. User risk profiles evolve over time as the system learns from behavior patterns, allowing legitimate users to transition from higher to lower scrutiny levels, improving ease of operation while maintaining security.
Data Source
AI summary
A computer-implemented method for access control includes detecting, by a security agent of a computing device, a request by a user of the computing device for access to a controlled asset. The method also includes authenticating, by the security agent, the user in response to the request. The method includes granting, by the security agent, access to the controlled asset based on determining that an initial risk score for the authenticated user does not exceed a predetermined threshold. Additionally, the method includes periodically calculating, by the security agent using at least one security policy and at least one machine learning model, an updated risk score of the authenticated user based on a behavior of the user. The method further includes performing a security action in response to determining the updated risk score exceeds the predetermined threshold. Various other methods, systems, and computer-readable media are also disclosed.