# Blockchain and AI: Securing Intelligent Networks for the Future

arXiv: https://arxiv.org/abs/2604.06323 · HTML: https://mlsanity.com/paper/2604.06323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 181 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [Prompting Large Language Models for Counterfactual Generation: An Empirical Study](https://mlsanity.com/paper/2305.14791/) (2023)
- [A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes](https://mlsanity.com/paper/2601.05293/) (2026)
- [AI for DevSecOps: A Landscape and Future Opportunities](https://mlsanity.com/paper/2404.04839/) (2024)
- [Engineering Trustworthy Machine-Learning Operations with Zero-Knowledge Proofs](https://mlsanity.com/paper/2505.20136/) (2025)
- [Enhancing Data Integrity and Traceability in Industry Cyber Physical Systems (ICPS) through Blockchain Technology: A Comprehensive Approach](https://mlsanity.com/paper/2405.04837/) (2024)
- [A Composable Game-Theoretic Framework for Blockchains](https://mlsanity.com/paper/2504.18214/) (2025)
- [MM-AttacKG: A Multimodal Approach to Attack Graph Construction with Large Language Models](https://mlsanity.com/paper/2506.16968/) (2025)

## Abstract (arXiv)

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.