Trustworthy Deep Learning for Cybersecurity: A Structured Review Across Detection, Robustness, Privacy, Explainability, and Deployment

Deep learning is increasingly used in cybersecurity to detect, classify, prioritize, and explain evidence from network traffic, logs, binaries, graphs, text, code, and multimodal telemetry. However, the literature remains fragmented across tasks, datasets, architectures, trustworthiness properties, and deployment settings, making it difficult to judge whether benchmark performance transfers to operational cyber defense workflows. This paper presents a structured narrative review with an evidence-oriented synthesis, not a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-counted systematic review. The synthesis uses a de-duplicated cited-source bibliography of 115 references as an evidence-mapping corpus; this corpus is reported for transparency and is not presented as a PRISMA final-inclusion set. The evidence map is organized through a five-axis framework: security task, data modality, model family, trustworthiness property, and deployment environment. In response to methodological and scope concerns common in broad survey work, the revision narrows the claims to a transparent cited-source synthesis, defines explicit inclusion boundaries, adds a data-charting codebook, reports non-exclusive coded emphasis matrices, and introduces practical tables for dataset selection, split protocols, deployment-reporting targets, and large language model (LLM)-enabled security operations center (SOC) risk controls. Across application areas, the reviewed literature indicates that benchmark accuracy is necessary but insufficient. Deployment readiness also depends on adversarial robustness, privacy protection, explainability, uncertainty calibration, drift handling, reproducibility, resource-aware resilience, and computational feasibility. The review identifies persistent gaps in temporal validation, cross-dataset testing, analyst-centered explanation, secure learning pipelines, agentic-LLM safety, and edge-aware deployment. The resulting research agenda emphasizes accurate, resilient, privacy-aware, explainable, reproducible, and deployable cybersecurity artificial intelligence systems.