区块链赋能的人工智能与AI代理在安全数据共享和网络安全应用中的应用
Blockchain-Enabled Artificial Intelligence and AI Agents for Secure Data Sharing and Cybersecurity Applications
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中文总结 AI 辅助
本文通过元综合研究，提出一种结合对抗性模型、区块链数据来源和智能合约多代理修复的分层架构，以解决AI安全运营中的数据、模型和代理信任问题。
中文摘要 AI 辅助
区块链和人工智能（AI）正在融合成一个单一的基础设施层，用于保护分布式系统中的数据共享、模型完整性和自主决策。本文提出了一项元综合研究，汇集了四项组成研究，涵盖对抗性机器学习、云环境中的AI驱动的异常检测、多代理大语言模型（LLM）流水线的自动漏洞修补，以及在其整个生命周期中保护AI系统的更广泛领域，并将其发现置于新兴的区块链赋能AI和自主AI代理文献中。每项组成研究都针对现代AI驱动的安全运营中一个不同的故障点：训练数据和模型行为的完整性、实时监控的可靠性以及自动代码修复的可信度。我们认为，区块链的不可变性、去中心化共识和可验证来源等特性直接解决了这三者共有的一个差距：在没有中央权威的情况下，建立对数据、模型和自主代理的信任的困难。基于区块链安全数据共享、联邦学习和多代理协调的实际研究，我们提出了一种分层参考架构，该架构将对抗性加固的模型、区块链锚定的数据来源、AI驱动的异常检测和智能合约治理的多代理修复相结合。最后，我们指出了在可扩展性、隐私-透明度权衡以及自主代理治理方面的开放问题，这些问题必须在这些集成系统能够在生产关键环境中被信任之前得到解决。
英文摘要
Blockchain and artificial intelligence (AI) are converging into a single infrastructural layer for securing data sharing, model integrity, and autonomous decision-making across distributed systems. This paper presents a meta-synthesis that draws together four constituent studies covering adversarial machine learning, AI-powered anomaly detection in cloud environments, automated vulnerability patching by multi-agent large language model (LLM) pipelines, and the broader landscape of securing AI systems across their lifecycle and situates their findings within the emerging literature on blockchain-enabled AI and autonomous AI agents. Each constituent study addresses a distinct point of failure in modern AI-driven security operations: the integrity of training data and model behavior, the reliability of real-time monitoring, and the trustworthiness of automated code remediation. We argue that blockchain's properties of immutability, decentralized consensus, and verifiable provenance directly address a gap common to all three: the difficulty of establishing trust in data, models, and autonomous agents that operate without a central authority. Building on real-world research on blockchain-secured data sharing, federated learning, and multi-agent coordination, we propose a layered reference architecture that couples adversarially hardened models, blockchain-anchored data provenance, AI-driven anomaly detection, and smart-contract-governed multi-agent remediation. We conclude by identifying open problems in scalability, privacy-transparency trade-offs, and the governance of autonomous agents that must be resolved before such integrated systems can be trusted in production-critical environments.
发表机构
- Palo Alto Networks（帕洛阿尔托网络公司）
机构由 AI 辅助整理，请以论文原文为准。
