Blockchain and AI: Securing Intelligent Networks for the Future

arXiv cs.AI / 4/10/2026

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Key Points

  • The paper synthesizes how blockchain and AI are used together to secure “intelligent networks,” noting that existing research is fragmented across ledger design, AI-based detection, cyber-physical systems, and agentic workflows.
  • It proposes three reusable contributions: a taxonomy for blockchain-AI security, integration patterns for verifiable and adaptive security workflows, and BASE, an evaluation blueprint/checklist covering AI quality, ledger behavior, service levels, privacy, energy, and reproducibility.
  • The work maps evidence across domains such as IoT, critical infrastructure, smart grids, transportation, and healthcare, finding that the conceptual fit is strong but real-world proof is uneven and often prototype-driven.
  • It clarifies role separation: blockchain can provide provenance, trust, and auditability, while AI supports detection, adaptation, and orchestration, and it outlines future research priorities like interoperable interfaces and privacy-preserving, bounded agent automation.

Abstract

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.