Blockchain and AI: Securing Intelligent Networks for the Future
Organizations: Department of Electrical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE · Department of Information Systems and Security,College of Information Technology, United Arab Emirates University, Al Ain 15551, UAE · Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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.
Figures & tables
| Reference | Primary scope | Intelligent-network security focus | LLM / agentic focus | Evaluation blueprint | What this paper adds beyond it |
| Salah et al. (2019) [ 6 ] | Blockchain for AI: applications, platforms, protocols, and open research challenges | Indirect; not centered on intelligent-network security workflows | No | No | Reframes the area around security workflow design, cross-domain evidence synthesis, and BASE |
| Hussain and Al-Turjman (2021) [ 9 ] | Broad review of AI and blockchain synergies, applications, and challenges | Broad and non-specific; not organized around intelligent-network security operations | No | No | Adds a security taxonomy, integration patterns, and explicit evaluation/reporting criteria |
| Ressi et al. (2024) [ 14 ] | AI enhancing blockchain technology, including consensus, smart contracts, data privacy, and efficiency | Focuses mainly on AI blockchain improvement rather than end-to-end security workflows | No explicit focus | No | Adds the complementary blockchain-AI security view, CPS and threat-operations emphasis, and BASE |
| Zuo (2025) [ 13 ] | Bidirectional AI-blockchain synergy and convergence across IoT applications and beyond | Closer to secure IoT settings, but still broader than intelligent-network security workflow design | No explicit focus | No | Adds explicit security workflow framing, LLM/agentic considerations, and an evidence/maturity map across domains |
| This paper | Taxonomy, integration framework, BASE, and evidence landscape for blockchain-AI security in intelligent networks | Yes; centered on verifiable, adaptive, and auditable security workflows | Yes | Yes | Unifies taxonomy, workflow design, benchmarking, and cross-domain maturity assessment in one reference paper |
| Consensus | Finality | Fault / attack tolerance | Latency | Energy profile | Typical fit for intelligent-network security |
| PoW | Probabilistic | Strong Sybil resistance, but vulnerable to majority-hash-power concentration and expensive to operate | High | High | Useful when open participation is required, but generally unsuitable for low-latency CPS and operational security pipelines |
| PBFT / IBFT | Deterministic | Tolerates up to one-third Byzantine nodes under standard BFT assumptions | Low | Low–moderate | Strong fit for permissioned critical infrastructure, industrial coordination, and audit trails where finality and bounded latency matter |
| PoA | Deterministic or near-deterministic, depending on implementation | Relies on vetted validators and governance accountability rather than open participation | Low | Low | Strong fit for regulated environments, consortium deployments, and cross-organizational security workflows with known participants |
| PoAh 2.0 | Deterministic within its permissioned workflow setting | Extends authority-based operation with AI-assisted dynamic authentication and sensitivity-aware trust decisions | Low | Low | Best suited to domain-specific settings such as IoMT-edge and adaptive trust management where authentication context changes over time |
| Dimension | Blockchain AI | AI Blockchain |
|---|---|---|
| Data Integrity & Provenance [ 137 , 134 , 139 ] | Provides tamper-proof training datasets, verifiable model updates, and immutable provenance trails that ensure AI operates on trustworthy data. | AI cannot change immutability guarantees; it improves upstream data quality via preprocessing and anomaly detection, which benefits on-chain provenance. |
| Transparency & Explainability [ 143 , 144 ] | Offers immutable audit logs of AI model decisions, enabling verifiable accountability. | Supports interpretability of blockchain records by applying explainable AI techniques to smart contracts and transaction analysis. |
| Scalability & Efficiency [ 134 , 137 , 133 ] | No direct effect on model scalability; blockchain can indirectly support scalable AI through trusted data sharing and provenance. | Optimizes blockchain consensus (e.g., resource-aware scheduling, reinforcement learning for adaptive consensus) and reduces computational overhead. |
| Security [ 137 , 145 , 146 ] | Ensures AI pipelines are resilient to data poisoning and tampering by anchoring evidence on-chain. | Detects anomalies, adversarial behavior, or malicious nodes within blockchain networks using machine learning models. |
| Trust [ 134 , 147 , 148 ] & Governance | Smart contracts enforce provenance, fairness, and policy compliance for AI models (e.g., in federated learning). | AI enables automated auditing and vulnerability detection in smart contracts, improving blockchain governance. |
| Layer | Minimum items to report | Representative measures |
|---|---|---|
| AI quality | Detection quality, false-alarm burden, calibration, drift sensitivity | AUROC, AUPRC, MCC, false positives/day, Brier score, time-to-detect |
| Ledger behavior | Finality-aware throughput, commit latency, audit latency, resilience | TPS at finality, commit time, orphan/reorg statistics, provenance query latency |
| Closed-loop security | Operational service levels under attack and failure | Detection-to-mitigation time, audit-to-action time, failure injection, poisoning tolerance |
| Privacy and governance | Privacy guarantees, policy traceability, operator accountability | , consent traceability, decision logs, approval checkpoints |
| Energy and cost | Resource footprint of the full pipeline | Per-inference energy, per-transaction energy, storage growth, GPU-hours, carbon-aware scheduling notes |
| Reproducibility | Inputs, scripts, environments, and workload definitions | Data splits, seeds, manifests, attack scripts, licenses, notebooks |
Cybersecurity
Cybersecurity develops methods to protect digital and cyber-physical systems from attacks, vulnerabilities, data loss, and misuse.
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