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• Risk: If identity-related data is used to train AI models, attackers may infer sensitive attributes or reconstruct original data through model inversion or membership inference attacks. 

  If the wallet uses AI services (e.g., for fraud detection, identity verification, or UX personalization), sensitive identity data might be exposed during model training or inference. If wallet operations involve external AI APIs, data could leak through logs or model updates. Even if raw data isn’t shared, patterns in queries or metadata could allow attackers to infer user attributes. e.g your AI tools on your mobile phone have access to your ID wallet!!

  
  

  If AI is integrated for convenience (e.g., chatbots or automated KYC), and those models access identity data, leakage and inference risks reappear. Metadata (timestamps, transaction patterns) can still be exploited for inference even if credentials are protected.

• Solution:
• Enforce
data minimization,
• data minimization→ wallet selective dislousure is already there to solve ths problem but by using AI data can reveal werden. (is it really a solution?)
• segregate sensitive datasets
,
• adopt privacy-preserving training methods* (e.g., differential privacy), and secure the entire data lifecycle.

Misinformation and Social Engineering

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1. A. Golda et al., "Privacy and Security Concerns in Generative AI: A Comprehensive Survey," in IEEE Access, vol. 12, pp. 48126-48144, 2024, doi: 10.1109/ACCESS.2024.3381611.  →  https://ieeexplore.ieee.org/document/10478883
2. 'THE EVOLUTION OF IDENTITY SECURITY IN THE AGE OF AI: CHALLENGES AND SOLUTIONS ', International Journal of Computer Engineering and Technology (IJCET)  Volume 16, Issue 1, Jan-Feb 2025, pp. 2305-2319, Article ID: IJCET_16_01_165 Available online at https://iaeme.com/Home/issue/IJCET?Volume=16&Issue=1 ISSN Print: 0976-6367; ISSN Online: 0976-6375; Journal ID: 5751-5249 Impact Factor (2025): 18.59 (Based on Google Scholar Citation) DOI: https://doi.org/10.34218/IJCET_16_01_165