Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities

Computer Science > Cryptography and Security [Submitted on 8 Sep 2025 (v1), last revised 15 Apr 2026 (this version, v2)] Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities Safayat Bin Hakim, Muhammad Adil, Alvaro Velasquez, Shouhuai Xu, Houbing Herbert Song Cybersecurity demands both rapid pattern recognition and deliberative reasoning, yet purely neural or purely symbolic approaches each address only one side of this duality. Neuro-Symbolic (NeSy) AI bridges this gap by integrating learning and logic within a unified framework. This systematic review analyzes 103 publications across the neural-symbolic integration spectrum in cybersecurity through April 2026, organizing them via a three-tier taxonomy -- deep integration, structured interaction, and contextual baselines -- and a Grounding-Instructibility-Alignment (G-I-A) analytical lens. We find that multi-agent and structured-integration architectures across the surveyed spectrum substantially outperform single-agent approaches in complex scenarios, causal reasoning enables proactive defense beyond correlation-based detection, and knowledge-guided learning improves both data efficiency and explainability. These findings span intrusion detection, malware analysis, vulnerability discovery, and autonomous penetration testing, revealing that integration depth often correlates with capability gains across domains. A first-of-its-kind dual-use analysis further shows that autonomous offensive systems in the broader survey corpus are already achieving notable zero-day exploitation success at significantly reduced cost, fundamentally reshaping threat landscapes. However, critical barriers persist: evaluation standardization remains nascent, computational costs constrain deployment, and effective human-AI collaboration is underexplored. We distill these findings into a prioritized research roadmap emphasizing community-driven benchmarks, responsible development practices, and defensive alignment to guide the next generation of NeSy cybersecurity systems. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2509.06921 [cs.CR]   (or arXiv:2509.06921v2 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2509.06921 Focus to learn more Submission history From: Safayat Bin Hakim [view email] [v1] Mon, 8 Sep 2025 17:33:59 UTC (684 KB) [v2] Wed, 15 Apr 2026 05:15:58 UTC (638 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2025-09 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)