NeuroGuard-X: A LangGraph - Orchestrated Autonomous Cybersecurity Framework Integrating Graph-Based AI Tools, Multistage NLP, Hybrid ML Detection, and Generative AI Reasoning

Cybersecurity threats have evolved from static malware to multi-stage AI-generated attacks capable of autonomously evading detection systems. Traditional signature-based and rule-Based systems fail to provide contextual reasoning, multi-event correlation, or real-time adaptive mitigation. This paper presents NeuroGuard-X, a next-generation autonomous cybersecurity framework that integrates graph-based AI tools, LangGraph multi-agent orchestration, multistage NLP fusion, traditional ML anomaly detection, and generative AI reasoning. Traditional ML models, including LSTM, Autoencoders, Graph Neural Networks (GNN), and XGBoost detects behavioral anomalies, whereas Generative AI models interpret, contextualize, and narrate threats into analyst-ready incident summaries. A graph-driven agentic architecture enables multi-step reasoning, attack-path reconstruction, and autonomous mitigation. Evaluated using CIC-IDS-2017, DARPA, MAWI, and a custom AI-augmented phishing dataset, NeuroGuard-X achieves 97.8% detection accuracy, 92.4% zero-day recall, 98.3% malicious email precision, and reduces SOC alert fatigue by 71%. The proposed system demonstrates that combining graph intelligence, hybrid ML, and LLM-based reasoning creates a powerful framework for modern cyber defense across digital payment and e-commerce ecosystems. Overall, NeuroGuard-X bridges the gap between accurate threat detection and trustworthy autonomous reasoning, enabling practical deployment in real-world security operations.