Blockchain and artificial intelligence (AI) are increasingly proposed together for securing intelligent networks, but the literature remains fragmented across ledger design, AI-driven detection, cyber-physical applications, and emerging agentic workflows. This paper synthesizes the area through three reusable contributions: (i) a taxonomy of blockchain-AI security for intelligent networks, (ii) integration patterns for verifiable and adaptive security workflows, and (iii) the Blockchain-AI Security Evaluation Blueprint (BASE), a reporting checklist spanning AI quality, ledger behavior, end-to-end service levels, privacy, energy, and reproducibility. The paper also maps the evidence landscape across IoT, critical infrastructure, smart grids, transportation, and healthcare, showing that the conceptual fit is strong but real-world evidence remains uneven and often prototype-heavy. The synthesis clarifies where blockchain contributes provenance, trust, and auditability, where AI contributes detection, adaptation, and orchestration, and where future work should focus on interoperable interfaces, privacy-preserving analytics, bounded agentic automation, and open cross-domain benchmarks. The paper is intended as a reference for researchers and practitioners designing secure, transparent, and resilient intelligent networks.

翻译：区块链与人工智能（AI）正日益被共同提出用于保障智能网络安全，但现有文献在账本设计、AI驱动检测、信息物理应用及新兴智能体工作流等领域仍较为分散。本文通过三项可复用贡献对该领域进行综合研究：（i）面向智能网络的区块链-AI安全分类体系，（ii）可验证与自适应安全工作流的集成模式，以及（iii）区块链-AI安全评估蓝图（BASE），该报告清单涵盖AI质量、账本行为、端到端服务等级、隐私、能耗及可复现性等维度。本文还描绘了物联网、关键基础设施、智能电网、交通运输及医疗健康等领域的实证图谱，表明概念契合度较高，但真实世界证据仍不均衡且多以原型验证为主。该综合研究阐明了区块链在溯源、信任和可审计性方面的贡献，AI在检测、自适应和编排方面的作用，并指出未来工作应聚焦可互操作接口、隐私保护分析、受限智能体自动化以及开放跨域基准测试。本文旨在为设计安全、透明且具韧性的智能网络的研究人员与实践者提供参考。