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02/08/2026 12:06 pm
TL;DR: Purpose-built AI models embedded in identity, authentication and age verification flows can complete checks in seconds, run on-device, and avoid collecting unnecessary data, according to Yoti. The governance lesson is that model scope, data minimisation, and independent signal composition now matter as much as speed, while layered liveness and document controls help reduce spoofing and deepfake risk.
NHIMG editorial — based on content published by Yoti: modular AI for identity verification and age assurance
By the numbers:
- One Yoti MyFace liveness detection check can use over 10 individual models.
Questions worth separating out
Q: How should organisations design identity verification flows for higher fraud risk?
A: They should use risk-based routing so the verification journey matches the transaction’s sensitivity.
Q: Why do layered biometric checks work better than a single liveness test?
A: Because attackers often target whichever signal is easiest to spoof.
Q: What do security teams get wrong about on-device AI processing?
A: They often assume local processing means no governance needed.
Practitioner guidance
- Define risk-based verification paths Map use cases to the minimum set of checks required for the transaction.
- Separate model governance by function Manage document authentication, face matching, liveness detection, and age estimation as independently testable controls.
- Test for presentation attacks continuously Validate PAD against replay, mask, printed-image, and synthetic-media attacks in production-like conditions.
What's in the full article
Yoti's full article covers the implementation detail this post intentionally leaves for the source:
- The exact four-step identity verification flow and how each model contributes to the final decision.
- The three-step age estimation flow, including Secure Image Capture and its role in injection attack detection.
- Examples of how the same modular design is adapted for different customer risk appetites and use cases.
- The operational testing approach behind model updates, independent evaluations, and ethics-led validation.
👉 Read Yoti's analysis of modular AI for identity verification and age assurance →
Modular identity AI: are smaller models changing verification controls?
Explore further
02/08/2026 1:11 pm
Modular verification is a governance pattern, not just a model design choice. When identity checks are split into discrete steps, teams gain more control over assurance levels, privacy exposure, and change management. That matters because identity risk is rarely uniform across all use cases. A bank, retailer, and age-gated service do not need the same evidence chain, so governance should reflect transaction risk rather than force one verification path everywhere.
A question worth separating out:
Q: Who is accountable when biometric identity checks are used for age or access decisions?
A: Accountability usually sits with the platform operator, because it decides what data is collected, how it is used, and what access or eligibility outcome follows. Where personal data or minors are involved, legal and privacy obligations become central. Governance should document ownership across security, legal, product, and fraud teams.
👉 Read our full editorial: Modular AI in identity verification reduces data exposure and spoofing risk
