Liveness Detection
Techniques used to verify that an input (e.g., biometric) originates from a live subject rather than a spoof or replay, enhancing system security and integrity.
Anti-spoofing measures - challenge-response protocols, motion analysis, texture analysis, or multi-modal checks - that distinguish genuine biometric inputs (face, voice) from photos, videos, or recordings. Governance requires regular liveness-test updates to counter evolving spoofing attacks, integration into authentication pipelines, and logging of failed attempts for security monitoring and compliance reporting.
A banking app’s face-login adds a liveness check: it prompts users to blink or turn their head, verifying real-time motion before unlocking. All liveness-check failures are logged and reviewed weekly by security analysts to detect new spoofing tactics and refine detection algorithms.

We help you find answers
What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
Who is Enzai built for?
How is Enzai different from other governance tools?
Can we start if we have no existing AI governance process?
Does AI governance slow down innovation?
How does Enzai stay aligned with evolving AI regulations?
Research, insights, and updates
Empower your organization to adopt, govern, and monitor AI with enterprise-grade confidence. Built for regulated organizations operating at scale.





