Adaptive AI Models for Real-Time Cyber Threat Attribution in Large-Scale Enterprise Networks

Abstract

Better real-time threat attribution systems are needed as enterprise-scale digital infrastructures evolve, increasing cyber threat complexity and volume. Innovative adaptive AI models may quickly and accurately identify attacker origins using telemetry, network data, and behavioral indications. Architectural frameworks, algorithms, and operational methodologies of adaptive AI-driven attribution systems in big corporate networks are examined in this article. Dynamic threat pattern detection and actor categorization in attacks are explored utilizing machine learning, deep learning, and hybrid analytics. Integration, data fusion, and performance assessment for attribution systems till January 2021 are also examined. Data heterogeneity, model interpretability, and hostile manipulation limit adaptive AI models' situational awareness, incident reaction delay, and proactive cybersecurity defenses.