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Web3 and AI Security

In: Web3 Applications Security and New Security Landscape

Author

Listed:
  • Jerry Huang

    (The University of Chicago)

  • Ken Huang

    (DistributedApps LLC)

  • Krystal Jackson

    (University of California, Berkeley)

  • Luyao Zhang

    (Duke Kunshan University)

  • Jennifer Toren

    (Cloud Security Alliance)

Abstract

This chapter examines the intersection of Web3 and AI in security, focusing on how they can enhance each other. It highlights AI’s potential for dynamic threat detection and adaptive security in Web3 environments. However, AI also introduces risks like generative output manipulation, overreliance, and ethical concerns. To address these, the chapter proposes governance frameworks encompassing model verification, multi-layered defense strategies, and responsible AI development leveraging Web3’s decentralized nature. Perspectives are provided on constraints for AI advancement, integrating AI in critical infrastructure, data-centric machine learning pipelines with DLT, model integrity via blockchain, and mitigating AI existential risk through Web3.

Suggested Citation

Handle: RePEc:spr:fuobcp:978-3-031-58002-4_8
DOI: 10.1007/978-3-031-58002-4_8
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