Artificial Intelligence and Blockchain Integration for Adaptive Threat Intelligence and Privacy-Preserving Data Governance
Keywords:
Anomaly Detection, Data Governance, Privacy Preservation, Smart Contracts, Threat Intelligence, Federated Learning, Blockchain, Decentralized AIAbstract
The convergence of Artificial Intelligence (AI) and Blockchain Technology (BCT) offers a transformative paradigm for addressing the twin challenges of adaptive threat intelligence and privacy-preserving data governance in distributed computing environments. Existing centralized architectures expose AI model training pipelines to data tampering, adversarial manipulation, and privacy violations that undermine the trustworthiness of automated decision systems [1,2]. This paper proposes a Decentralized AI-Blockchain (DAI-BC) framework that embeds federated machine learning within a permissioned blockchain infrastructure governed by smart contracts [3,9]. The framework introduces a five-layer security architecture spanning IoT data acquisition, AI threat intelligence, smart contract policy enforcement, immutable ledger coordination, and domain-specific analytics. A novel adaptive trust weighting mechanism adjusts the influence of each federated participant based on continuously monitored behavioral metrics recorded on-chain. Experiments conducted across healthcare, finance, and industrial IoT deployment scenarios demonstrate 95.8% anomaly detection accuracy, a 12.3% false alarm rate reduction over standalone federated learning, and a privacy preservation score of 93.1. The framework sustains detection rates above 90% under 45% adversarial node presence and achieves a 54.2% reduction in communication overhead compared to centralized baselines [4,5].
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