AI-Driven Cyber Threat Detection and Response Framework for Securing Digital Systems

Abstract

This paper presents an AI-driven cyber threat detection and response framework designed to improve the identification, prioritization, explanation and containment of malicious activity in heterogeneous digital systems. Contemporary environments generate network flows, endpoint events, authentication records, application logs and cloud telemetry at a scale that makes purely rule-based monitoring difficult to maintain. The proposed framework combines data ingestion, feature engineering, supervised machine learning, risk scoring, explainable AI and response orchestration in a continuous security loop. The design is aligned conceptually with the risk-management orientation of the NIST Cybersecurity Framework 2.0 and the trustworthiness considerations of the NIST AI Risk Management Framework. The framework also uses the MITRE ATT&CK knowledge structure as a basis for translating model outputs into recognizable adversarial behaviors. For empirical validation, the paper specifies a reproducible protocol using the CICIDS2017 intrusion-detection dataset, which contains benign traffic and multiple contemporary attack categories and more than 80 network-flow features. Rather than reporting fabricated experimental measurements, the study defines the evaluation procedure, metrics, comparison baselines, ablation tests and statistical reporting requirements needed for a defensible implementation. The contribution is therefore a complete research framework that can be implemented and experimentally evaluated without treating illustrative values as observed results. The proposed architecture emphasizes detection quality, response latency, interpretability, operational safety and feedback-driven model maintenance, providing a practical foundation for securing networked digital systems.