Blockchain and AI: Securing Intelligent Networks for the Future

TL;DR

This paper synthesizes blockchain and AI security for intelligent networks, providing a taxonomy, integration patterns, and an evaluation blueprint to guide future research and practical implementations.

Contribution

It offers a comprehensive taxonomy, integration patterns, and a security evaluation blueprint for blockchain-AI systems in intelligent networks.

Findings

Strong conceptual fit across domains but uneven real-world evidence

Blockchain provides provenance, trust, and auditability

AI offers detection, adaptation, and orchestration capabilities

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.