Zero Trust Engine for Threshold-Based Cyberthreat Remediation
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Solution Overview
Problem
Conventional methods of cyberthreat remediation are inefficient and resource-intensive, requiring significant time and resources for frequent remediation actions, making networks and applications susceptible to cyberthreats when actions are performed infrequently.
Innovation Solution
A computing platform with a zero trust engine that receives cyberthreat and vulnerability information, generates cyberthreat mappings, and initiates remediation actions only when the cyberthreat level indicator satisfies a threshold, using machine learning to efficiently identify and address cyberthreats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional remediation actions are performed frequently, then network security reliability is improved, but resource consumption and time cost increase significantly
Solution Approach 1:
The system performs preliminary vulnerability scanning and cyberthreat modeling to identify potential security issues before they can be exploited. By proactively detecting vulnerabilities and assessing cyberthreat levels continuously, the system prepares remediation actions in advance rather than reacting to actual attacks, thus maintaining high security reliability without requiring frequent disruptive remediation interventions
Solution Approach 2:
The system implements continuous feedback loops through automated vulnerability scanning, cyberthreat level assessment, and remediation verification. The cyberthreat level indicator provides real-time feedback on security status, enabling the system to adjust remediation frequency dynamically - performing actions only when thresholds are exceeded, thereby maintaining security while minimizing unnecessary resource consumption and time loss
2Reliability
If conventional remediation actions are performed frequently, then network security reliability is improved, but resource consumption increases
Solution Approach 1:
Instead of performing full remediation actions on all applications continuously, the system applies partial monitoring and assessment to all applications while reserving intensive remediation resources only for those exceeding cyberthreat thresholds. The automated system performs lightweight vulnerability scanning and threat level calculation on all applications, then concentrates computational resources only on high-risk targets, significantly reducing overall resource consumption while maintaining security
Solution Approach 2:
The system implements self-service capabilities through automated vulnerability detection, cyberthreat assessment, and remediation execution. The automated remediation system can independently identify vulnerabilities, calculate cyberthreat levels, select appropriate remediation actions, and verify effectiveness without requiring continuous human intervention or extensive manual resource allocation, thereby reducing operational resource consumption while maintaining high security reliability
3Loss of energy
If remediation actions are performed infrequently, then resource consumption is reduced, but network security reliability deteriorates
Solution Approach 1:
The system dynamically adjusts remediation frequency and intensity based on real-time cyberthreat level indicators. Rather than using fixed schedules, the system continuously monitors vulnerability data and cyberthreat metrics, automatically increasing remediation frequency when threats exceed thresholds and reducing frequency when risks are low. This dynamic adaptation allows the system to maintain high security reliability during high-risk periods while conserving resources during low-risk periods
4Measurement precision
If manual review of each application is performed, then measurement precision of cyberthreat risk is improved, but productivity decreases
Solution Approach 1:
The system introduces an automated intermediary layer that performs initial vulnerability scanning, cyberthreat modeling, and risk assessment for all applications. This automated intermediary processes large volumes of applications quickly, then presents only those exceeding predefined thresholds to human reviewers for final verification. This intermediary filtering mechanism maintains high measurement precision by ensuring human review focuses on genuine high-risk cases while dramatically improving overall productivity by automating the bulk assessment work
Data Source
AI summary
Aspects related to cyberthreat remediation using a zero trust engine are provided. A cyberthreat remediation platform may receive information of cyberthreats and vulnerabilities for application associated with a network. The platform may train a zero trust engine to generate cyberthreat mappings comprising vulnerability-cyberthreat pairings based on the information. The platform may generate a cyberthreat record for an application based on a cyberthreat mapping. The platform may generate a cyberthreat level indicator for the application based on the cyberthreat record. The platform may compare the cyberthreat level indicator to a threshold to identify whether remediation actions should be initiated. Based on the comparison, the platform may initiate one or more remediation actions to resolve one or more cyberthreats.