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- Risk: Many current digital identity security systems lack the scalability and accuracy required to handle large volumes of users and increasingly sophisticated AI-based attacks. This limitation poses a significant challenge for identity wallets operating at national or cross-border scale.
- Solutions:
Deployment of scalable and resilient system architectures
Use of AI to automate threat detection and response
Continuous improvement of algorithmic accuracy under high-load conditions [2]
Risks as AI-as-a-Service
GenAI: here we mean using GenAI outside the wallet (AI-as-a-Service)
Implicit data leakage (even without “sending data”)
Even if you think you’re only sending:
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This is called inference leakage. Over time, the AI provider can reconstruct who you are and what you’re doing — without seeing raw identity data.
Loss of user sovereignty
When AI runs outside the wallet:
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This quietly breaks self-sovereign identity principles.
Policy manipulation & dark negotiation
External AI can:
bias disclosure decisions
“optimize” for platform goals
subtly over-disclose to reduce friction
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This is algorithmic coercion, not a bug. + Explainable Generative AI*
Prompt and context retention
Most AI services:
log prompts
retain context
reuse data for tuning or monitoring
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Once logged: You can’t revoke it.
Correlation across wallets and services
A single AI provider serving many wallets can:
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In addition, if the AIaaS provider experiences an outage, millions of users could be locked out of essential services simultaneously.
Regulatory and jurisdictional drift 
External AI services may:
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Data may be stored in servers under foreign laws, complicating compliance with national regulations like GDPR and creating legal uncertainty.
Model hallucination becomes a security risk
Inside a wallet:
AI mistakes are bounded
Outside:hallucinated policy interpretations
incorrect legal assumptions
wrong proof selection
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Hallucination here is not UX noise — it’s identity damage.
"Black Box" Opacity
Complex AI models are not transparent. Users can be denied access (e.g., false non-match) without a clear, explainable reason or recourse.
Algorithmic Bias & Discrimination
AI models can inherit biases, leading to unfair denials of access for specific demographic groups. The system is also vulnerable to adversarial attacks designed to fool it.
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- 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
- '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