Decentralized Information-Security Processes and Systems for Dynamic, Artificial-Intelligence Centric, Auto-Generative Protection Against Cross-Channel Threat in Multi-Modal Networks

A real-time information-security process for preventing cross-channel threats in a decentralized finance (DeFi) multi-modal network comprising the steps of:

• receiving, by a computing machine (CM), identification of a cross-channel threat vector for the DeFi multi-modal network;
• communicating, by the CM to a consortium of independent nodes, the cross-channel threat vector;
• generating, by a consensus algorithm amongst the CM and the consortium, agreement on the cross-channel threat vector;
• extracting, by the CM from a live model library for channels including mobile, credit, debit, and online, metadata for threat-vector mitigation models;
• analyzing, by the CM based on deep learning of the metadata, permutations of the threat-vector mitigation models to identify a model configuration with a highest propensity to prevent the cross-channel threat vector;
• selecting, by the CM from the live model library, an optimized set of the threat-vector mitigation models corresponding to the model configuration;
• auto-generating, dynamically by artificial intelligence on the CM based on the optimized set, information-security rules for the model configuration;
• storing, by the CM in a distributed ledger blockchain, the information-security rules to prevent the cross-channel threat vector;
• and
• deploying, by the CM, the information-security rules to prevent caused the cross-channel threat vector, whereby fraud is prevented in the DeFi multi-modal network.

A real-time information-security system for preventing cross-channel threats comprising:

• a threat-vector identification platform for identifying a fraud threat in a decentralized finance (DeFi) multi-modal network including at least a mobile channel and an online channel;
• a live model library containing threat-vector models to attempt to prevent the fraud threat, said live model library coupled to the threat-vector identification platform;
• a photonic quantum computing machine (PQCM) to detect changes in the live model library, extract metadata from the live model library for the threat-vector models, analyze the metadata by deep learning, and compute permutations of the threat-vector models to generate a model configuration with a highest propensity to prevent the fraud threat;
• a consensus module used by the PQCM to achieve consensus amongst consortium nodes on the model configuration for the model configuration most likely to prevent the fraud threat;
• a blockchain retrieval module for the PQCM to extract rule sets in a distributed ledger blockchain corresponding to the threat-vector models for the model configuration;
• a dynamic rule-generation module for the PQCM to dynamically generate fraud control rules derived from the rule sets extracted from the distributed ledger blockchain by the blockchain retrieval module;
• a blockchain storage module for the PQCM to store, in the distributed ledger blockchain, the fraud control rules that were dynamically generated by the dynamic rule-generation module for the model configuration;
• a blockchain notification module for the PQCM to provide the consortium nodes with access information to access the fraud control rules that were stored in the distributed ledger blockchain;
• and
• a deployment module for the PQCM to deploy the fraud control rules to payment platforms to prevent the fraud threat in the DeFi multi-modal network.