Topic starter
02/08/2026 12:19 pm
TL;DR: Deepfakes now enable employee impersonation, account takeover, synthetic identity fraud, and real-time injection attacks that can defeat human judgment and some biometric checks, according to Yoti. The practical issue is not whether the media looks fake, but whether identity verification, MFA, and liveness controls can still establish trust under live fraud conditions.
Editorial analysis by NHI Mgmt Group, based on content published by Yoti: “The rising challenge of detecting deepfakes”.
Key questions
Q: What breaks when identity verification depends on the user spotting fraud?
A: The control fails when the person being protected becomes the detection layer.
Q: Why do deepfakes make traditional authentication weaker?
A: Deepfakes weaken traditional authentication because they imitate the human signals that many approval processes still trust, including voice and video.
Q: How do security teams evaluate whether liveness detection is strong enough?
A: Look for measurable resistance to presentation attacks, defined false accept and false reject rates, and testing that reflects the real environment where the control will run.
Practitioner guidance
- Harden verification beyond visual checks Require additional proof before accepting a video, voice, or image as evidence of identity.
- Add liveness checks to high-risk flows Use liveness detection for onboarding, account recovery, and any interaction where a remote participant can influence access or payment decisions.
- Protect the capture path from injection Validate whether a camera feed has been overridden or substituted before it reaches the decision engine.
Bottom line: Deepfakes have turned appearance and voice into weak trust signals for identity and fraud teams.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
02/08/2026 1:20 pm
Visual trust is no longer a control boundary. The article shows that identity programmes built on “if it looks like the user, it is the user” have crossed a practical limit. Deepfakes now let an attacker manufacture the human signal that many verification flows were designed to trust. The implication is that identity assurance must shift from appearance-based judgment to stronger proof of presence and control.
A few things that frame the scale:
- According to a recent Gartner report, 40% of government organizations are expected to establish dedicated trust operations functions by 2028 to combat deepfakes.
A question worth separating out:
A: Organisations should treat deepfake fraud as a multi stage risk, not just an onboarding problem. Effective controls combine liveness checks, document verification, behavioural signals, step up review, and ongoing monitoring through the customer lifecycle. Teams also need clear escalation paths for high risk cases, because synthetic media can defeat single point checks and create false confidence in identity assurance.
👉 Read our full editorial: Deepfakes are testing identity controls beyond visual trust