ML Security Policy Correlation for Explainable Access Control
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Solution Overview
Problem
Existing network security systems struggle with dynamically adjusting security policies in response to changing user, device, and network conditions, leading to either lenient policies that fail to detect security issues or strict policies that trigger false positives, disrupting operations.
Innovation Solution
A machine learning model, such as a transformer model, dynamically adjusts security policies based on input features, and a model explanation system provides explainability by correlating input features to the generated security policy, allowing for adaptive and transparent security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static security policy is implemented, then the security system is simple to manage, but it cannot adapt to changing conditions leading to either too lenient or too strict policies
Solution Approach 1:
The patent implements dynamic security policies that automatically adjust based on real-time analysis of user behavior, device characteristics, network conditions, and threat intelligence. The system transitions from static, pre-defined policies to dynamic policies that are continuously optimized through machine learning models, enabling adaptation to changing conditions without manual intervention.
Solution Approach 2:
The system incorporates continuous feedback loops where security events, user behaviors, and threat intelligence are fed back into the policy optimization engine. This feedback mechanism enables the system to learn from past security incidents and continuously refine security policies, improving adaptability while maintaining manageable complexity through automated closed-loop control.
2Reliability
If a strict security policy is applied, then security detection capability is improved, but false positives increase disrupting operations
Solution Approach 1:
The system applies different security policy strictness levels to different users, devices, and contexts based on their specific risk profiles. Instead of a uniform strict policy, the machine learning models analyze individual characteristics and apply appropriately tailored security measures, maintaining high detection capability for high-risk entities while minimizing disruptions for low-risk entities.
Solution Approach 2:
The system dynamically adjusts security policy parameters such as authentication requirements, monitoring intensity, and access restrictions based on real-time risk assessments. The machine learning models continuously optimize these parameters to achieve the right balance between detection capability and operational continuity, reducing false positives while maintaining security effectiveness.
3Productivity
If a lenient security policy is applied, then operational disruption is reduced, but security issues are not detected
Solution Approach 1:
The system implements dynamic security policies that automatically tighten or relax based on real-time threat levels and user behavior patterns. During normal operations, policies remain lenient to maintain productivity, but automatically become stricter when anomalies or threats are detected, ensuring security issues are detected without unnecessary operational disruption during normal conditions.
4Adaptability or versatility
If machine learning models are used to dynamically adjust security policies, then adaptability is improved, but explainability of security decisions decreases
Solution Approach 1:
The system introduces an explanation layer that acts as an intermediary between the machine learning models and security operators. This layer translates complex model predictions into human-understandable justifications, showing which factors influenced policy decisions and why. The intermediary maintains the benefits of dynamic ML-based policy adjustment while preserving explainability through structured interpretation of model outputs.
Data Source
AI summary
In some examples, an authorization controller includes a machine learning model to manage access control to a network environment by a client device based on input features to the machine learning model, the input features including user information of a user of the client device, device information representing the client device, and network information representing a network used by the client device. The machine learning model when executed by the authorization controller generates a security policy used by the authorization controller in managing the access control. A system can correlate the security policy to model parameters set by the machine learning model in generating the security policy, and use the correlation to indicate which of the input features contributed to the security policy generated by the machine learning model.