Blockchain for Identity Theft Prevention in Digital AI Applications

Abstract

Escalating identity theft—fueled by large‐scale data breaches, AI-assisted social engineering, and deepfakes—undermines trust in digital systems and especially in AI-driven applications where automated agents transact, personalize content, and make consequential decisions. This manuscript proposes a standards-aligned, privacy-preserving reference architecture that uses blockchain to anchor decentralized identifiers (DIDs), verifiable credentials (VCs), selective-disclosure cryptography, and revocation registries; combines device-bound, phishing-resistant authentication (passkeys); and integrates content provenance signals for AI outputs. We synthesize the state of the art (W3C DID/VC, OpenID4VCI/OpenID4VP, ISO/IEC 18013-5 mDL, C2PA, NIST SP 800-63 and 800-207, ENISA), then present a methodology—BC-Guard—for securing AI user and agent identity life cycles: enrollment, authentication, authorization, transaction attestation, and post-event audit. A qualitative results section analyzes expected risk reductions for common identity-theft attack paths (credential phishing, account takeover, synthetic identity KYC fraud, and real-time deepfake impersonation). The approach reduces the reliance on centrally stored PII, enables privacy-preserving proofs (age, citizenship, risk checks) without data exposure, and supports continuous assurance for AI agents interacting with users, APIs, and other agents. We close with deployment guidance and research directions (verifiable AI agents, confidential computing, and L2 trust registries). (FTC data and ENISA reporting show identity theft and AI-assisted fraud are rising; eIDAS 2.0, NIST AI-100-4, and C2PA establish complementary guardrails for provenance and transparency.)