Integration Challenges in Blockchain-Based AI Model Deployment

Abstract

The promise of combining blockchain with artificial intelligence (AI) is compelling: auditable data provenance for training sets, tamper-evident logging for model lifecycle events, decentralized marketplaces for models and datasets, and automated enforcement of usage policies via smart contracts. Yet organizations quickly discover that operationalizing blockchain-based AI goes beyond stitching together two popular technologies. Differences in trust assumptions, latency and throughput profiles, security primitives, compliance expectations, and tooling maturity frequently collide at deployment time. This manuscript organizes those frictions into a coherent integration problem space and proposes a reference architecture and evaluation methodology to reason about trade-offs. We review the literature on blockchain consensus and scalability, privacy-preserving machine learning (federated learning, differential privacy, secure computation, and zero-knowledge proofs), data governance and compliance (e.g., GDPR), and MLOps platforms. We then present a methodology that stress-tests seven integration dimensions: architecture and partitioning (on-chain vs. off-chain responsibilities), performance and cost (latency, throughput, gas), privacy and confidentiality (leakage risks and mitigations), security and integrity (tamper-evidence, oracle trust), interoperability (heterogeneous chains and toolchains), compliance and governance (auditability versus erasure rights), and human/organizational fit (DevOps, incident response, and skills).