AI-powered zero-trust cyber defense system for secure financial transaction networks

Abstract

An AI-driven zero-trust cyber defense system for secure financial transaction networks, the system being characterized by comprising: a. an agent-based AI security orchestration layer (100) comprising a plurality of autonomous AI agents deployed on a distributed computing infrastructure, each agent being configured to independently collect security data, plan multi-stage investigation and remediation actions via clearly defined APIs, self-correct based on downstream feedback, communicate via an asynchronous message bus, and maintain a common agent memory in which intermediate states of inference and historical action logs are persistently stored; b.a search-based security information engine (110) that is operationally coupled with the agentic AI security orchestration layer (100) and configured to encode query requests into dense vector representations using a transformer-based embedding model, perform semantic nearest-neighbor searches against a vector index of the threat intelligence knowledge base (180), and append the retrieved threat information documents as extended context to the prompts forwarded to the large language model threat analysis module (120); c.a large language model threat analysis module (120) that is functionally coupled with the search-based security information engine (110) and configured to perform multi-stage, thought-based inferences using enriched threat telemetry to generate structured threat assessments that include threat classification, confidence scores, impact severity assessments, indicators of compromise, and prioritized remediation recommendations; d.a gateway for enforcing zero-trust policies (130) positioned at each transaction boundary and configured to aggregate a composite trust signal from an identity trust score, a transaction risk score, a current threat level, and a policy context vector, and to make access decisions for each transaction request in the form of allow, enhanced authentication, or deny; e. a continuous identity and device verification engine (140) functionally coupled with the zero-trust policy enforcement gateway (130) and configured to perform continuous authentication by capturing biometric behavioral signals and device state parameters at configurable intervals during active sessions, calculating an identity trust score, and triggering enhanced authentication if this score deteriorates significantly; f.a behavioral anomaly detection unit (150) that is functionally coupled with the agent-based AI security orchestration layer (100) and configured to create and maintain dynamic behavioral baseline profiles using autoencoder neural networks and isolation forest models, and to compute anomaly scores for each new behavioral observation; g. a real-time transaction risk assessment engine (160) that is functionally coupled with the zero-trust policy enforcement gateway (130) and configured to compute a composite transaction risk score by combining a behavioral anomaly sub-score, an identity trust sub-score, a network context sub-score, and a threat context sub-score, which is provided by the large language model threat analysis module (120) using a gradient-enhanced ensemble model; h.a module for encrypted communication and data integrity (170) that provides a secure communication infrastructure connecting all modules and enforcing mutual TLS encryption with post-quantum cryptographic suites and cryptographic message authentication codes in all communication between the modules; i. a threat intelligence knowledge base (180) that includes curated threat intelligence feeds, historical incident reports, attacker TTP matrices, regulatory compliance requirements, and vulnerability signatures, maintained in a vector-indexed repository and continuously updated via automated capture pipelines; and j.a console for automating security operations (190) that is operationally coupled with the Agentic AI security orchestration layer (100) and provides security personnel with monitoring of autonomous agent actions, manual override options, visualization of compliance audit paths, and natural language threat assessment displays generated by the large language model threat analysis module (120).