AI-Driven Forensic Systems for Real-Time Anomaly Detection and Threat Mitigation in Cybersecurity Infrastructures

As cyber threats grow in frequency and complexity, traditional rule-based and reactive defense mechanisms are proving inadequate for protecting critical digital infrastructures.This paper presents a comprehensive overview of AI-driven forensic systems designed to enhance real-time anomaly detection and threat mitigation across diverse cybersecurity environments.The study begins by examining the broader landscape of cybersecurity, emphasizing the need for continuous monitoring, adaptive learning, and scalable defense frameworks in the face of zero-day exploits, insider threats, and polymorphic attacks.It then narrows its focus to the integration of artificial intelligence in digital forensics, where machine learning and deep learning models are employed to automatically classify anomalous behavior, detect intrusion patterns, and anticipate future attack vectors.We explore how AI can augment forensic capabilities by enabling proactive rather than post-incident response.Key system components-including data ingestion pipelines, intelligent agents, neural detection layers, and decision-support modules-are discussed in detail.Emphasis is placed on unsupervised learning models, generative AI, and hybrid architectures that fuse knowledge-based rules with statistical anomaly detection.In addition, the paper reviews real-world applications in network security, endpoint monitoring, and cloud environments, alongside challenges such as model interpretability, adversarial robustness, and data labeling constraints.To validate the feasibility and effectiveness of such systems, a reference architecture is proposed, highlighting the role of AI in automating evidence collection, correlating multi-source logs, and supporting rapid incident response.Finally, the work outlines future directions involving federated learning, explainable AI, and the integration of threat intelligence with autonomous defensive operations.