How Does Deepfake Detection Fit Secure Meetings on Zoom and Microsoft Teams?

How does deepfake detection fit secure meetings?

Deepfake detection fits secure meetings at three points. Before the meeting, identity controls decide who can join: single sign-on, verified domains, lobbies and named invitations. During the meeting, analysis of sampled video frames and audio can flag a participant whose face or voice appears synthetic. After the meeting, recordings can be analysed in depth when a decision is disputed or an incident is suspected.

None of those points authorizes anything. A meeting can explain a request, but a payment, a credential reset or a data release should be confirmed in the system that owns it, by a person reached on a known channel. Detection's job is to raise the alarm early and to produce evidence later.

This framing matters because the meeting is now a common delivery channel for impersonation. Attackers send a calendar invite from a lookalike or compromised account, join with a face swap or a cloned voice, and use the feeling of a face-to-face conversation to get past doubts that an email would raise.

What real meeting deepfakes have looked like

The Arup fraud is the clearest example of a meeting used as the decisive step. Hong Kong police said a finance employee joined a conference call with what looked like the chief financial officer and other colleagues, then transferred HK$200m to five local bank accounts in 15 transactions. Arup confirmed it was the company involved in May 2024.

Political targets have seen the same technique. In September 2024 Senator Ben Cardin, then chair of the Senate Foreign Relations Committee, received an email that appeared to come from former Ukrainian foreign minister Dmytro Kuleba and joined a Zoom call with someone who looked and sounded like him. According to the Senate security office's notice, as reported by The New York Times and summarized by European Pravda, the caller asked politically charged questions about the upcoming election and long-range strikes into Russia. Cardin ended the call and reported it to the State Department.

In both cases the participant's questions or request were what gave the game away, or should have. Cardin noticed the questions did not fit. The Arup employee had initially suspected the email but was reassured by the faces on the call.

How do you detect deepfakes in Zoom or Microsoft Teams meetings?

You detect deepfakes in Zoom or Microsoft Teams meetings by combining platform controls, a verification habit and media analysis. Start with platform settings: restrict external participants, use the lobby, show verified domain labels and require authenticated join for sensitive meetings. Then make it normal to verify any request made in a meeting through a second channel, such as a call to a directory number or an approval inside the finance system.

Media analysis comes third. For recorded meetings, export the recording and analyse the video and audio after the call. For live meetings, a bot or media integration can sample frames and short audio windows and send them to a detection API. Human cues such as odd lighting on the face, mouth movement out of sync with speech, or a refusal to turn sideways can prompt a check, but they are unreliable against good real-time face swaps.

Simple live challenges still help when a meeting feels wrong: ask the participant to answer a question only the real person would know from a private conversation, or end the call and ring them back on a known number.

Live meetings, recorded meetings and the latency question

Live analysis has to work within the meeting's media path. A typical design samples a frame every few seconds per participant and short audio windows, sends them to an API, and raises an alert to the host or security team if scores cross a threshold. Latency per request matters, but sampling rate and alert handling matter more: an alert that arrives ten seconds into a call is useful, one that arrives after the payment is not.

Analysing media should not slow the meeting itself. Detection runs beside the stream rather than in it, so participants see no delay. The cost is coverage: a sampled approach can miss short manipulations, which is why recordings of sensitive meetings deserve a full pass afterwards.

Recorded analysis is slower but stronger. The whole file can be examined frame by frame and segment by segment, results can be reviewed by an analyst, and the output can be attached to an incident record or a legal file.

Neural Defend's approach to meeting media

Neural Defend's NeuroDetect handles the recorded side. Security, fraud or legal teams upload an authorized meeting recording, audio file or screenshot into a case and review heatmaps and attention views down to individual frames and audio segments. An LLM-assisted summary describes the findings in plain language linked to the evidence, the analyst makes the final call, and a downloadable forensic report records observations, uncertainty, provenance and model context.

Teams building their own live integration can send sampled frames or short clips to NeuroVerify, which checks a face image, video or audio in one API call and returns a unified risk score from six signals, including Face Manipulation Detection and Live Presence Verification. Neural Defend states under 200 ms p95 latency for eligible image requests under defined test conditions. On the public VendorBench-100 benchmark of difficult still images, Neural Defend ranked first of 36 systems with an MCC of 0.876; that benchmark does not measure video, audio or live calls.

Meeting recordings often contain confidential material, so deployment matters. Neural Defend offers managed cloud, private cloud or VPC, on-premises and air-gapped deployment, and holds ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 42001 and SOC 2 Type II certifications.

Procedures that make meetings harder to fake

Write down which decisions may never be finalised in a meeting alone, typically payments above a threshold, new beneficiaries, access changes and disclosure of sensitive data. Give every employee explicit permission to pause a meeting and verify, including when the requester appears to be a senior executive.

Record sensitive meetings where law and policy allow, keep recordings long enough for disputes to surface, and decide in advance who can request a forensic review. Run a tabletop exercise each year in which a fake executive joins a call, and measure how long it takes for someone to stop the action and verify.