cs.CRApr 7, 2026

Blockchain and AI: Securing Intelligent Networks for the Future

Authors: Joy Dutta, Hossien B. Eldeeb, Tu Dac Ho

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

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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

Figure 1: Taxonomy of blockchain-AI security and paper roadmap: Each category maps to the main sections of the paper
ReferencePrimary scopeIntelligent-network security focusLLM / agentic focusEvaluation blueprintWhat this paper adds beyond it
Salah et al. (2019) [ 6 ]Blockchain for AI: applications, platforms, protocols, and open research challengesIndirect; not centered on intelligent-network security workflowsNoNoReframes 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 challengesBroad and non-specific; not organized around intelligent-network security operationsNoNoAdds 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 efficiencyFocuses mainly on AI →\rightarrow blockchain improvement rather than end-to-end security workflowsNo explicit focusNoAdds 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 beyondCloser to secure IoT settings, but still broader than intelligent-network security workflow designNo explicit focusNoAdds explicit security workflow framing, LLM/agentic considerations, and an evidence/maturity map across domains
This paperTaxonomy, integration framework, BASE, and evidence landscape for blockchain-AI security in intelligent networksYes; centered on verifiable, adaptive, and auditable security workflowsYesYesUnifies taxonomy, workflow design, benchmarking, and cross-domain maturity assessment in one reference paper
Table 1: Positioning this paper relative to representative prior surveys already cited in the manuscript
ConsensusFinalityFault / attack toleranceLatencyEnergy profileTypical fit for intelligent-network security
PoWProbabilisticStrong Sybil resistance, but vulnerable to majority-hash-power concentration and expensive to operateHighHighUseful when open participation is required, but generally unsuitable for low-latency CPS and operational security pipelines
PBFT / IBFTDeterministicTolerates up to one-third Byzantine nodes under standard BFT assumptionsLowLow–moderateStrong fit for permissioned critical infrastructure, industrial coordination, and audit trails where finality and bounded latency matter
PoADeterministic or near-deterministic, depending on implementationRelies on vetted validators and governance accountability rather than open participationLowLowStrong fit for regulated environments, consortium deployments, and cross-organizational security workflows with known participants
PoAh 2.0Deterministic within its permissioned workflow settingExtends authority-based operation with AI-assisted dynamic authentication and sensitivity-aware trust decisionsLowLowBest suited to domain-specific settings such as IoMT-edge and adaptive trust management where authentication context changes over time
Table 2: Consensus trade-offs for security-critical intelligent-network deployments [ 15 , 16 ]
Figure 2: Overview of AI-driven techniques for enhanced cybersecurity
Figure 3: Layered Security Architecture for Blockchain-AI Integrated Systems
Figure 4: Overview of proactive threat detection and response strategies enabled by the integration of blockchain and AI
Figure 5: Visual breakdown of key challenges in integrating blockchain and AI for intelligent network security
DimensionBlockchain →\rightarrow AIAI →\rightarrow 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 ] & GovernanceSmart 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.
Table 3: Comparative perspectives on Blockchain-AI synergy
LayerMinimum items to reportRepresentative measures
AI qualityDetection quality, false-alarm burden, calibration, drift sensitivityAUROC, AUPRC, MCC, false positives/day, Brier score, time-to-detect
Ledger behaviorFinality-aware throughput, commit latency, audit latency, resilienceTPS at finality, commit time, orphan/reorg statistics, provenance query latency
Closed-loop securityOperational service levels under attack and failureDetection-to-mitigation time, audit-to-action time, failure injection, poisoning tolerance
Privacy and governancePrivacy guarantees, policy traceability, operator accountability(ε,δ)(\varepsilon,\delta) , consent traceability, decision logs, approval checkpoints
Energy and costResource footprint of the full pipelinePer-inference energy, per-transaction energy, storage growth, GPU-hours, carbon-aware scheduling notes
ReproducibilityInputs, scripts, environments, and workload definitionsData splits, seeds, manifests, attack scripts, licenses, notebooks
Table 4: BASE checklist for reporting blockchain-AI security systems [ 143 , 56 , 129 , 134 , 124 , 137 , 128 , 133 , 141 , 80 ]
Figure 6: Emerging trends and future directions in securing intelligent networks through blockchain and AI integration
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