Decentralized AI Security Rules for Cross-Channel Threat Mitigation
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Solution Overview
Problem
Decentralized networks face significant security risks due to the lack of communication and coordinated threat-vector control among members, leading to ineffective isolation-based security measures and increased vulnerability to cross-channel threats, which can combine information from multiple channels to create more dangerous threats.
Innovation Solution
A decentralized information-security process utilizing photonic quantum computing machines (PQCM) and generative AI to auto-generate threat-vector optimized solutions, dynamically derive cross-channel threat-vector rules, achieve consensus, and deploy security rules across a consortium of entities through distributed-ledger blockchains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decentralized networks use isolation-based security measures, then individual security control is maintained, but coordinated threat-vector control is lost and cross-channel threats can exploit multiple channels independently
Solution Approach 1:
The patent merges individual security controls with coordinated threat response by implementing a decentralized system where multiple autonomous entities share threat intelligence and coordinate their security measures. The system combines isolation-based security with collaborative threat-vector control through a shared knowledge base and coordinated response mechanism, allowing entities to maintain independence while responding collectively to cross-channel threats.
Solution Approach 2:
The system implements a universal security framework that serves multiple functions simultaneously: individual entity security control, cross-channel threat detection, coordinated response, and knowledge sharing. The decentralized architecture allows each entity to perform its own security functions while also contributing to and benefiting from the collective security intelligence across all channels.
2Ease of operation
If decentralized networks operate in isolation, then operational independence is maintained, but threat-vector control effectiveness is reduced due to lack of communication among members
Solution Approach 1:
The patent implements feedback mechanisms where security intelligence, threat vectors, and response effectiveness information are shared among decentralized entities. Each entity operates independently but receives feedback from the collective system about threats detected in other channels, allowing them to adjust their security measures while maintaining operational independence. This feedback loop continuously improves threat-vector control effectiveness without requiring centralized control.
3Reliability
If real-time threat detection and response is implemented, then cross-channel threats are mitigated faster, but computational complexity and processing requirements increase significantly
Solution Approach 1:
The system segments the computational tasks across multiple decentralized entities rather than concentrating complexity in a single system. Each entity performs local threat detection and analysis independently, then shares results with the collective system. This segmentation distributes the computational burden and reduces the complexity requirement for any single entity while maintaining real-time threat mitigation capability across all channels.
Data Source
AI summary
Decentralized information-security (IS) mitigates against cross-channel threats vectors with a photonic quantum computing machine (PQCM). Threat signals are communicated to consortium members. Live model libraries regarding the threats, characteristics, metadata, model solutions, etc. are in distributed ledgers. PQCM extracts metadata and analyzes permutations to identify configuration(s) with the highest propensity to mitigate the threat. PQCM determines optimized set(s) of the threat-vector mitigation models for the configuration(s). PQCM auto-generates, dynamically by AI/ML based on the optimized set, IS rules for the configuration. Updated threats, threat characteristics, model configurations, IS rules, etc. for the cross-channel threat can be stored in distributed ledger blockchains, shared with consortium members, and deployed to prevent the threat. Nodal consensus algorithms may be used to reach agreement amongst consortium members to independently confirm threat signal validity, best model combinations, highest propensity scores, and proposed dynamically generated IS rules to address the threat.