Securing AI Supply Chains with Blockchain and ML-Based Threat Detection

Abstract

The increasing integration of Artificial Intelligence (AI) into critical sectors such as healthcare, finance, banking, and autonomous systems has exposed significant vulnerabilities in AI supply chains. These supply chains, which encompass data collection, model training, and deployment, are susceptible to various threats, including data tampering, model poisoning, and adversarial attacks. Traditional security mechanisms often fall short in providing the necessary transparency, traceability, and real-time threat detection required to safeguard these complex systems. To address these challenges, this paper introduces a novel framework that synergistically combines blockchain technology for immutable and decentralized record-keeping with machine learning (ML)-based threat detection for dynamic and real-time anomaly identification. Our approach aims to secure the entire AI lifecycle, from data provenance to model deployment, by leveraging blockchain's tamper-proof ledger capabilities and ML's predictive analytics for threat detection. Through extensive experimentation, we demonstrate that our framework significantly enhances supply chain integrity, mitigates vulnerabilities, and achieves a 15-20% improvement in attack detection accuracy compared to conventional security methods. The results highlight the potential of integrating blockchain and ML to create a robust and scalable security solution for AI supply chains.