Distributed Cyber Resilience Verification with Compliance Tokens
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Solution Overview
Problem
Existing cybersecurity systems in distributed entity or third-party networks (DETPNs) face challenges in maintaining real-time adaptability and scalability due to static assessments and delayed reactive evaluations, leading to insufficient monitoring and validation of cyber resilience, particularly in complex and heterogeneous environments.
Innovation Solution
Implementing automated monitoring and real-time compliance evaluation systems that generate digital compliance tokens and use graph neural networks to track and validate cybersecurity states across DETPNs, enabling dynamic adjustments and proactive threat management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automated monitoring and real-time compliance evaluation systems are implemented, then adaptability and response time to cybersecurity threats are improved, but device complexity and implementation cost increase
Solution Approach 1:
The system segments compliance verification into discrete timing phases (initial, periodic, event-triggered) and separates monitoring functions into modular components that can be independently deployed and managed, reducing overall system complexity while maintaining real-time adaptability
Solution Approach 2:
The system performs preliminary compliance assessments and establishes baseline security states before actual cybersecurity threats occur, enabling proactive threat management and reducing the complexity of reactive responses
2Measurement precision
If comprehensive monitoring of all entities in DETPN is performed, then measurement precision of compliance status is improved, but loss of time and computational resources increase
Solution Approach 1:
The system implements periodic compliance evaluations at predetermined timing phases rather than continuous monitoring, achieving sufficient measurement precision while significantly reducing time loss and computational resource consumption
Solution Approach 2:
The system applies different monitoring intensities and evaluation frequencies to different entities based on their risk profiles and compliance criticality, optimizing the balance between measurement precision and time efficiency
3Reliability
If digital compliance tokens and graph neural networks are used to track cybersecurity states, then reliability of compliance validation is improved, but device complexity and processing requirements increase
Solution Approach 1:
The system creates digital compliance tokens as simplified representations of complex compliance states, enabling reliable tracking and validation without requiring the full complexity of the underlying security configurations to be processed continuously
Solution Approach 2:
Graph neural networks serve as intermediary processing layers that automatically analyze and validate compliance relationships between entities, reducing the complexity burden on human operators and manual verification processes
Data Source
AI summary
Systems, methods, and computer-readable storage media for verification and validation of cyber resilience in a distributed entity or third-party network (DETPN). One system includes one or more processing circuits including memory and at least one processor configured to access or identify compliance data for at least one of a plurality of entities or third-parties, the compliance data corresponding with a first timing phase. The at least one processor further configured to access or identify at a second timing phase updated compliance data for at least one of the plurality of entities or third-parties based at least on environmental data of the DETPN. The at least one processor further configured to generate one or more tokens comprising at least one of the compliance data or the updated compliance data. The at least one processor further configured to provide the one or more tokens.