Artificial Intelligence for Cybersecurity Analytics: A Review of Deep Learning, Hybrid Architectures, and Operational Resilience
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Abstract
This review examines how artificial intelligence is reshaping cybersecurity analytics across three tightly connected domains: threat detection, explanatory decision support, and operational resilience. Rather than treating AI as a stand-alone detection tool, the paper positions it as a layered analytical infrastructure that links telemetry collection, representation learning, anomaly scoring, malware and botnet recognition, threat correlation, analyst triage, and governance. The review synthesizes evidence from classical machine learning, deep learning, graph learning, transformers, explainable AI, federated intelligence, and early quantum-enhanced approaches. It argues that the main contribution of AI in cybersecurity lies not only in higher predictive accuracy but also in the ability to connect descriptive, predictive, and prescriptive analytics under fast-changing threat conditions. The paper further shows that operational value depends on data quality, benchmark realism, false-alarm control, model interpretability, lifecycle monitoring, and the fit between algorithmic outputs and security workflows. The strongest systems are not those that maximize laboratory metrics in isolation, but those that combine robust representations, calibrated uncertainty, explainability, and human oversight. Future directions are identified in multimodal cyber intelligence, continual and causal learning, graph-native detection, privacy-preserving collaboration, and adaptive architectures that treat resilience as an organizational capability rather than a model score.
