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A Framework for Monitoring and Analyzing Artificial Intelligence Activities within Blockchain Systems
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Keywords
Artificial intelligence
blockchain systems
monitoring framework
privacy-preserving, distributed ledger
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Abstract
The rapid convergence of artificial intelligence (AI) and blockchain technologies has introduced transformative opportunities in distributed computing, secure data management, and autonomous decision-making. However, monitoring and analyzing AI activities within blockchain networks remains a critical challenge due to the decentralized nature of blockchains and the opaque execution of AI models. This paper proposes a comprehensive framework designed to systematically track, evaluate, and optimize AI operations on blockchain platforms. By integrating privacy-preserving mechanisms, consensus-aware monitoring, and activity profiling, the framework ensures secure and transparent AI performance analysis. Empirical evaluation through conceptual scenarios demonstrates its effectiveness in enhancing traceability, mitigating risks of malicious AI behaviors, and supporting compliance requirements. The proposed framework provides a foundational methodology for researchers and practitioners seeking to unify AI accountability with blockchain integrity.
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