Shufti’s AWS-hosted face liveness audit enables institutions to reassess spoofed, replayed, and injected sessions previously marked live using modern detection
LONDON, UNITED KINGDOM, January 27, 2026 /EINPresswire.com/ — Shufti, a global identity verification and fraud prevention provider, has launched a face liveness blind-spot audit engine on AWS Marketplace, giving regulated institutions a way to review historic liveness sessions for spoofed, replays, and injected activity all within their own AWS environments.
Face liveness controls in many organisations were deployed before replay tools, virtual camera feeds, and injection attacks became part of day-to-day fraud. Approvals made under those earlier conditions are still used to support onboarding, account recovery, device binding, and higher-risk transactions.
The Shufti audit engine is intended to recheck those past live sessions against current, modern verification standards of what constitutes a genuine, real-time client presence.
The liveness blind-spot audit is provided as an Amazon Machine Image (AMI) that runs inside the company’s own AWS account and Virtual Private Cloud.
It re-analyses stored liveness artefacts from historic sessions and highlights patterns associated with spoofing or injected streams, focusing on journeys where the presence of a live person is central to trust decisions.
All processing takes place within the company’s existing cloud account. Personally identifiable information does not leave the environment. It is intended for real-time analysis during live onboarding and can be scoped by time period, product, or customer cohort. Results may support governance reviews, remediation planning, or updates to existing verification policies.
Procurement, legal and security teams can treat the deployment as a defined review under existing AWS Marketplace terms rather than a new production integration.
For fraud, compliance, and internal audit teams, the output is a clearer view of where liveness-related weaknesses may sit in the existing customer base. Sessions that exhibit anomalies can be prioritised for further investigation or monitoring and can inform adjustments to current liveness standards or surrounding controls.
“Many liveness controls that were acceptable when they were deployed were not built with today’s spoofing and injection tools in mind,” said Shahid Hanif, Chief Executive Officer of Shufti. “A dedicated liveness audit like Shufti face liveness blind-spot audit on AWS Marketplace allows institutions to recheck historic live sessions with enhanced spoofing and replay attack detection, , entirely inside their own AWS environment, and see where past approvals may need closer attention.”
The liveness blind-spot audit forms part of Shufti’s KYC Blind Spot Audit suite on AWS Marketplace, alongside facial deepfake, document deepfake, and document originality audits, which together focus on historic KYC identity verification risk rather than live customer acquisition alone.
Distributed through AWS Marketplace, the audit can be deployed as a contained internal workload without impacting production systems or customer experience.
To learn more about the Shufti Liveness Blind Spot Audit, visit here: https://shuftipro.com/blind-spot-audit/liveness-detection/
About Shufti
Shufti is a global identity verification and fraud prevention provider supporting regulated organisations across financial services, fintech, igaming, e-commerce, and digital platforms. The innovator develops verification systems designed to operate across diverse jurisdictions, document types, and risk environments.
Shufti’s technology supports face biometric verification, document analysis, and identity risk assessment, with deployment models that allow processing within customer-controlled cloud infrastructure, including AWS.
Built with privacy and regulatory requirements in mind, Shufti enables organisations to assess identity risk, support governance objectives, and strengthen trust in digital onboarding and authentication processes worldwide.
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Shufti on AWS Marketplace: Liveness Spoof Detection Without Moving Data
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