Adaptive Trust Access Using Network Causality and Integrity States
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Solution Overview
Problem
Existing Zero Trust Architectures (ZTA) do not adequately account for network information flow in the computation of risk-based access authorization, failing to leverage network activities for adaptive trust enforcement.
Innovation Solution
A system and method for Continuous Adaptive Trust (CAT) access enforcement that models causal relationships between network activities and core entities, using telemetry data to determine integrity states and confidence scores for dynamic access control, incorporating provenance graphs and knowledge graphs to propagate latent cybersecurity states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If network information flow is not considered in risk-based access authorization computation, then the access control system is simpler to implement, but the security adaptability and accuracy of trust enforcement deteriorates
Solution Approach 1:
The patent segments the access control system into distinct functional modules: telemetry collection components, provenance graph management components, knowledge graph management components, and access control decision components. Each module handles specific aspects of network information flow analysis, allowing the system to achieve high security adaptability while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces multiple dimensional representations of network information: provenance graphs for tracking data flow origins and paths, knowledge graphs for semantic relationships between entities, and confidence scores for risk assessment. This multi-dimensional approach enables comprehensive security adaptability by analyzing network information flow from numerous perspectives simultaneously.
2Reliability
If continuous computation of adaptive confidence scores is implemented, then the risk-based authorization level reaches ultimate CAT, but the computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-building provenance graphs and knowledge graphs from historical network data, and by pre-defining access control policies and confidence score thresholds. This preparation allows the continuous CAT computation to proceed more efficiently using pre-processed information and established decision criteria, reducing real-time computational burden while maintaining high authorization reliability.
Solution Approach 2:
The patent implements feedback mechanisms where confidence scores and trust assessments continuously inform access control decisions, which in turn generate new telemetry data that updates the provenance and knowledge graphs. This closed-loop feedback enables the system to adapt to changing network conditions dynamically while optimizing computational resources by focusing processing on high-risk scenarios rather than continuous full-system analysis.
Data Source
AI summary
One example method includes receiving telemetry data including network activities of a network. The network activities are linked to core network entities. Causal relationships between the network activities and the core network entities are continually modeled to determine a propagation dynamic for each of the core network entities. The propagation dynamic includes integrity states that estimate a risk of each core network entity to the network. A request for access to the network is received from one of the core network entities. A confidence score is determined based at least on an access policy of the network and the integrity states of the core network entity requesting access. It is then determined if access to the network is to be granted based on the confidence score.