Cyber Restoration Engine for Autonomous Node Recovery
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Solution Overview
Problem
Existing cyber security systems lack interaction and adaptation between the stages of incident response, leading to inefficiencies in identifying, mitigating, and recovering from cyber threats.
Innovation Solution
An AI-based cyber security system with multiple engines that cooperate to identify, mitigate, and restore from cyber threats, utilizing a cyber security restoration engine that takes autonomous remediation actions to return compromised nodes to a trusted operational state, supported by a tracking component and communication module for external backup and recovery services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional linear incident response stages are used without interaction between stages, then the system structure is simple and easy to implement, but the response efficiency and adaptability to cyber threats deteriorate
Solution Approach 1:
The patent merges the traditionally separate linear incident response stages into an integrated system where detection, mitigation, and restoration engines work together through continuous interaction. The graph-based representation unifies multiple security functions, allowing information to flow between stages and enabling coordinated response actions that improve overall productivity while managing complexity through structured integration.
Solution Approach 2:
The patent implements feedback loops where the restoration engine provides information back to the detection and mitigation engines, and where operational states of nodes are continuously tracked and used to adjust response actions. This feedback mechanism enables the system to adapt to evolving threats and improve response efficiency by learning from each incident response cycle.
2Loss of time
If manual intervention is used for each incident response stage, then the system is easier to control and monitor, but the time required for detection, mitigation, and recovery increases
Solution Approach 1:
The patent implements self-service capabilities where the system automatically detects threats, determines appropriate mitigation actions, executes restoration procedures, and updates its knowledge base without requiring manual intervention at each stage. The AI-driven engines autonomously navigate the incident response process, significantly reducing recovery time while maintaining high levels of control through structured decision-making frameworks.
Solution Approach 2:
The patent prepares restoration actions in advance by maintaining a graph of possible remediation paths and pre-configured recovery procedures. When a threat is detected, the system can quickly execute pre-planned restoration actions, reducing the time needed for response while the automation framework manages the complexity of coordinating these actions across multiple system components.
3Measurement precision
If comprehensive tracking of all node operational states is implemented, then the accuracy of threat detection and remediation improves, but the computational resources and system complexity increase
Solution Approach 1:
The patent segments the system into discrete nodes represented in a graph structure, where each node's operational state is tracked independently. This segmentation allows the system to focus computational resources on tracking specific nodes of interest rather than continuously monitoring entire systems, improving measurement precision for critical elements while reducing overall computational burden through targeted state tracking.
Solution Approach 2:
The patent applies different levels of state tracking granularity to different nodes based on their importance and risk profile. Critical nodes receive more detailed monitoring and state tracking, while less critical nodes use simplified tracking methods. This local quality approach optimizes the balance between measurement precision and computational resource consumption by allocating tracking resources where they provide the most value.
Data Source
AI summary
A cyber security restoration engine takes one or more autonomous remediation actions to remediate one or more nodes in a graph of a system being protected back to a trusted operational state in order to assist in a recovery from the cyber threat. The cyber security restoration engine has a tracking component the operational state of each node in the graph of the protected system. The communication module also cooperates with the cyber security restoration engine to communicate with at least one of an external backup system and a recovery service to invoke backup remediation actions and/or recovery remediation actions to remediate one or more nodes potentially compromised by the cyber threat back to a trusted operational state, for example the state before the detected compromise by the cyber threat occurred in the protected system.