Адаптивний ШІ для кібербезпеки: практичні приклади усунення сліпих зон

Abstract

Cyber-attacks increasingly evade static, rules-based controls by shifting content, infrastructure, and pace. This article synthesizes practical machine-learning patterns that measurably improve defence across six domains: phishing/social engineering, malware detection, network anomaly detection, insider-risk analytics, vulnerability prioritisation, and incident-response automation. The approach highlights transformer-based NLP that reads messages more like people do (with reported F1 scores of approximately 0.98 on public phishing benchmarks), image-based CNNs that recognise malware “byte-textures,” autoencoders and sequence models that baseline network behaviour, federated and explainable methods for privacy-preserving insider detection, EPSS-driven triage that prioritises by exploitation likelihood, and reinforcement learning that adapts response actions under guardrails. Emphasis is on deployable patterns – shadow-mode pilots, precision/recall tracking, false-positive budgets, human-in-the-loop review, and continuous learning from user feedback and honeypot telemetry – so organisations can move from brittle signature races to adaptive systems that improve with every campaign observed. The transition to 5G and emerging 6G architectures compounds these challenges, introducing ultra-low latency requirements, massive device densities, and decentralized, edge-based infrastructures. Adaptive AI must therefore operate not only in traditional enterprise networks but also in heterogeneous, mobile, and resource-constrained 5G/6G environments where security, privacy, and resilience are paramount