AIAuditTrack: A Framework for AI Security system
This addresses security and risk traceability issues for organizations using AI in multi-agent environments, though it appears incremental as it builds on existing blockchain and identity technologies.
The paper tackles the problem of security and accountability in AI-driven applications by proposing AIAuditTrack, a blockchain-based framework for recording AI usage traffic and enabling governance, which demonstrated feasibility and stability with blockchain TPS metrics under large-scale interaction recording.
The rapid expansion of AI-driven applications powered by large language models has led to a surge in AI interaction data, raising urgent challenges in security, accountability, and risk traceability. This paper presents AiAuditTrack (AAT), a blockchain-based framework for AI usage traffic recording and governance. AAT leverages decentralized identity (DID) and verifiable credentials (VC) to establish trusted and identifiable AI entities, and records inter-entity interaction trajectories on-chain to enable cross-system supervision and auditing. AI entities are modeled as nodes in a dynamic interaction graph, where edges represent time-specific behavioral trajectories. Based on this model, a risk diffusion algorithm is proposed to trace the origin of risky behaviors and propagate early warnings across involved entities. System performance is evaluated using blockchain Transactions Per Second (TPS) metrics, demonstrating the feasibility and stability of AAT under large-scale interaction recording. AAT provides a scalable and verifiable solution for AI auditing, risk management, and responsibility attribution in complex multi-agent environments.