Zero Trust Access Policies With ML Threat Remediation
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Solution Overview
Problem
Traditional IT security models rely on trusting entities within a protected perimeter, which can lead to insider threats and data breaches, and are inadequate for distributed cloud environments.
Innovation Solution
A zero trust authentication and authorization system that verifies each user and device attempting to access IT resources, using machine identities and policies, with features like least privilege access, just-in-time access, and continuous machine learning for threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional perimeter-based security models are used to protect IT resources, then entities within the protected perimeter are trusted by default, but this leads to insider threats and data breaches
Solution Approach 1:
The patent segments the trusted perimeter into individual identity-based access controls. Instead of trusting all entities within a network perimeter, the system divides access control into discrete identity verification and policy enforcement points, applying zero trust principles to each access request independently.
Solution Approach 2:
The patent inverts the traditional security model by assuming no entity is trusted by default. Rather than trusting entities within the perimeter and verifying outsiders, the system verifies all entities regardless of location and only grants trust through explicit authentication and authorization policies.
2Reliability
If zero trust authentication and authorization is implemented to verify each user and device, then security is enhanced and insider threats are reduced, but system complexity increases
Solution Approach 1:
The patent implements a universal authentication and authorization system that handles multiple functions through unified components. The identity verification, policy enforcement, and access control mechanisms serve multiple purposes across different contexts, reducing overall system complexity despite the comprehensive security approach.
Solution Approach 2:
The patent introduces intermediary components such as identity providers, policy decision points, and policy enforcement points that mediate between users/devices and protected resources. These intermediaries simplify the complexity by centralizing verification logic and providing standardized interfaces.
3Object-affected harmful factors
If least privilege access policies are applied to control permissions, then exposure of sensitive data is minimized, but access control management becomes more complex
Solution Approach 1:
The patent implements dynamic access control policies that automatically adjust permissions based on context such as user identity, device state, location, and risk assessment. Rather than static least privilege assignments, the system dynamically modifies access rights in real-time, reducing data exposure while simplifying management through automated policy enforcement.
Solution Approach 2:
The patent incorporates feedback mechanisms where access decisions are continuously monitored and evaluated. The system receives feedback from policy enforcement points and uses this information to adjust policies, ensuring least privilege is maintained while adapting to changing conditions without manual intervention.
4Reliability
If machine learning models are used for continuous threat detection, then security vulnerabilities are detected in real-time, but computational resources and processing time increase
Solution Approach 1:
The patent applies machine learning models selectively rather than continuously to all data streams. The system uses partial action by triggering ML analysis only when specific conditions are met, such as anomalous behavior patterns or high-risk contexts, reducing computational resource consumption while maintaining effective threat detection.
Solution Approach 2:
The patent applies different levels of analysis to different data sources and contexts. Rather than uniform ML processing everywhere, the system applies sophisticated ML models only where needed based on local risk characteristics, while using simpler rules-based approaches for lower-risk scenarios, optimizing resource usage.
Data Source
AI summary
In some implementations, a zero trust system may deploy one or more policies for controlling access to a service associated with a first machine entity. The zero trust system may issue a machine identity that uniquely identifies a workload associated with a second machine entity. The zero trust system may receive telemetry data related to interactions between the service associated with the first machine entity and the machine identity that uniquely identifies the workload associated with the second machine entity. The zero trust system may use a machine learning model to detect a security threat associated with the first machine entity and/or the second machine entity according to the telemetry data. The zero trust system may update the one or more policies to remediate the security threat. The zero trust system may provide the one or more updated policies to the first machine entity.