AI-Augmented Cybersecurity Systems

Abstract

AI-augmented cybersecurity systems address the growing gap between the volume and sophistication of cyber threats and human analysts capacity to detect and respond. This study evaluates six AI approaches for cybersecurity - supervised intrusion detection using gradient-boosted trees, unsupervised anomaly detection via autoencoders for zero-day threat identification, NLP-based threat intelligence using transformer models, reinforcement learning for automated incident response, graph neural networks for lateral movement detection in authentication graphs, and LLM-assisted security operations for analyst triage augmentation -- across five cybersecurity benchmarks including CICIDS-2017, UNSW-NB15, EMBER malware classification, a custom enterprise authentication graph with simulated APT campaigns, and a 12-week SOC analyst productivity study with 48 security professionals. A total of 1,800 experiments were conducted. Gradient-boosted trees achieved the highest intrusion detection F1 (0.968 +- 0.004 on CICIDS-2017), outperforming deep neural networks (0.954) on tabular network flow features. Autoencoder anomaly detection identified 72.4 +- 4.8% of simulated zero-day attacks at 2.8% false positive rate by learning compressed representations of normal traffic. GNN-based lateral movement detection achieved AUROC = 0.924 +- 0.012 on enterprise authentication graphs, outperforming per-event classifiers by 15.6 points through multi-hop contextual pattern recognition. LLM-assisted SOC operations reduced mean alert triage time by 42.6 +- 8.4% and improved analyst accuracy on complex multi-source alerts by 12.8 +- 4.2 percentage points. No single AI approach addresses all cybersecurity needs; layered deployment combining supervised detection for known attacks, anomaly detection for zero-day threats, graph analysis for advanced persistent threats, and LLM triage for analyst productivity provides comprehensive defence. A practical AI cybersecurity deployment framework mapping threat landscape, SOC maturity level, and available security data to recommended AI capabilities is proposed.