AI social engineering detection: Defense mapped to the attack you are facing
Diopter detects AI-generated and manipulated media (voice and video) on live calls. It also evaluates identity, payment instructions, conversation and visual patterns and policy alignment so your team has one verdict they can act upon.
What is AI social engineering detection?
AI social engineering combines AI-generated media and social engineering tactics to influence a person into taking action.
The voice might sound like your CFO, the video might show your CEO, or on the other end there might be a real vendor contact pushing for a change in bank details. The approach may change, but the attack tends to follow a familiar path: authority, urgency, isolation, escalation, then the ask.
AI social engineering detection looks at that whole interaction rather than one signal at a time.
Diopter covers four capability areas: video, voice, identity and payment, and policy alignment.
Video
Video checks for synthetic and manipulated video across the call. Every participant is scored separately, not just the person speaking.
Voice
Voice checks the audio for traces left by voice-cloning and text-to-speech models. The call is scored throughout, so a voice that appears only when the request is made is also evaluated.
Identity and payment
Identity and payment look at who is involved and what is being requested. Beneficiary's banking information is also validated through accessible proprietary data systems.
Policy alignment
Policy alignment checks the request against the organization's set policies and processes. If a caller is asking someone to bypass an approval step, change a beneficiary, reset access, or make an exception, that becomes part of the assessment.
A single-layer tool can see only part of the call. For example, a video detector can tell you that a face looks synthetic. It cannot tell you whether a real person is using urgency to push through a wire.
That is the gap AI social engineering detection is designed to cover and Diopter brings these signals together and scores the interaction as it unfolds.
What attacks does Diopter detect?
Diopter covers nine solution areas across its capabilities and use cases.
Capabilities
Video deepfake detection
Score synthetic and manipulated video on every participant, live.
ExploreAudio deepfake detection
Detect cloned and synthetic voices on conference and phone calls.
ExplorePayment & identity verification
Confirm who is on the call and validate payment instructions.
ExplorePolicy alignment
Map calls to your controls and surface asks that bypass your approval requirements.
ExploreUse cases
Candidate fraud
Catch synthetic and fraudulent candidates before they reach payroll.
ExploreFinancial wire fraud
Verify the people and the payment instructions before a transfer clears.
ExploreVendor impersonation
Stop attackers posing as vendors from redirecting payments.
ExploreExecutive impersonation
Defend approvals from attackers posing as your executives.
ExploreHelp desk defense
Defend IT support agents from social engineering targeting credentials and access.
ExploreWhere Diopter runs in your business
Calls can become high-risk situations when they can lead to moving money, granting access or building/breaching the trust of your team.
Think about a few common situations.
A vendor calls to confirm new banking details, someone claiming to be from an IT support team asks your team member to grant or share access, or an apparently genuine-looking candidate joins a video interview.
All these may take different channels or attack different business areas, but the attack pattern usually remains similar.
A familiar identity may establish authority, topped with urgency that may reduce the time available for verification. The conversation then moves away from the regular process, leading to a bigger request and finally the ask.
Diopter runs alongside these calls from the first second. It works across Zoom, Microsoft Teams, Google Meet, Webex, VoIP and conference phone calls.
The call feeds into the Diopter layer. Identity, synthetic media, and conversation arcs are scored continuously. It then provides a verdict with evidence and policy signals, which can route to your console or existing workflow through webhooks, SIEM or tickets.
Email is used as input context only, never as an email-protection product. Verdicts and scores route to your console or workflow.
How Diopter works across video, voice and identity
Diopter brings four capability areas into one detection layer: video, voice, identity and payment, and policy alignment.
Consider this:
A CFO's voice may sound right, and the video may appear clear. But the person may be asking to push an exception to the regular approval process, transfer immediate funds or show urgency to change the beneficiary and close the conversation. A clean signal in one area does not override risk in another. It's only when they occur together that these signals matter.
Alternatively, a synthetic voice or manipulated video can be detected on a live call but without making any high-risk request. This doesn't necessarily convert the conversation into a high-risk threat.
Simply put, video and voice show what is happening in the media. Identity and payment add context about the people involved and what the call is asking them to do, and policy alignment shows whether that request fits the controls already in place.
What happens when Diopter flags a call
When Diopter flags a call, it gives your team more than a verdict. It gives them context.
Diopter continuously evaluates the interaction for signals of fraud, impersonation, social engineering, and other high-risk behavior.
When a risk threshold is crossed, Diopter provides a clear verdict under "Verified", "Potential Threat", "Suspected Threat", or "High-Risk Threat". This way, teams can understand what's happening while there's still time to act. Each comes with a confidence level rather than a clear yes or no.
Finally, the call can be flagged for review, routed to a security team, held for additional verification, or blocked.
There's no guesswork, just clean, actionable signals.
From signal to action.
Designed to reduce false alarms from isolated synthetic signals while still catching high-pressure social engineering from real people.
Are the voice and video frames synthetic, or a real human?
Is the dialogue being shaped toward authority, urgency, isolation, and an irreversible ask?
Real human on the line, request flow looks normal. No synthetic media, no pressure pattern.
Real human voice, but the conversation is being shaped toward an irreversible ask through urgency, authority framing, and off-policy timing. A social-engineering attempt by a real person, or a coached insider.
Synthetic voice or deepfake video detected, but the conversation isn't pushing toward a high-risk action. Often a benign AI agent, voice filter, or early-stage probe.
Synthetic media plus the arc is closing on a wire, MFA reset, credential hand-off, or hire. Both axes confirm.
| AI Detection | Arc Risk | What this looks like on a call | Verdict | Recommended action |
|---|---|---|---|---|
| Clean | Low | Real human on the line, request flow looks normal. No synthetic media, no pressure pattern. | Verified | Allow |
| Clean | High | Real human voice, but the conversation is being shaped toward an irreversible ask through urgency, authority framing, and off-policy timing. A social-engineering attempt by a real person, or a coached insider. | Potential Threat | Flag for review |
| Synthetic | Low | Synthetic voice or deepfake video detected, but the conversation isn't pushing toward a high-risk action. Often a benign AI agent, voice filter, or early-stage probe. | Suspected Threat | Hold for verification |
| Synthetic | High | Synthetic media plus the arc is closing on a wire, MFA reset, credential hand-off, or hire. Both axes confirm. | High-Risk Threat | Block |
Light to deploy, clear about what runs where.
Diopter works alongside your existing infrastructure. It adds a layer of detection and sends actionable results to the systems your security team already uses.Pilot in days, roll wider through MDM, and keep sensitive call media inside your perimeter.
- On-prem and hybrid deployments supported
- No caller-side install
- Bot or bot-free capture
- Configurable retention, including ZDR
- MDM rollout (Intune, Jamf)
- SOC 2 Type II in progress
FAQs
AI social engineering detection scores a live call on two axes at once: whether the voice and video are synthetic, and whether the conversation is being shaped toward an irreversible ask through authority, urgency, isolation and escalation. A single-signal tool can tell you a face looks generated. It cannot tell you that a real person is using urgency to push a wire through outside your approval process. Detection covers the whole interaction and resolves it into one verdict with a confidence level.
Diopter evaluates four capability areas, namely, video, voice, identity and payment, and policy alignment. It can score synthetic and manipulated video and audio, validate people and payment requests, and check whether the request bypasses the policy controls. It gives a verdict with a confidence level rather than presenting a simple yes or no answer.
Diopter runs on Zoom, Microsoft Teams, Google Meet, and Webex, as well as VoIP and conference phone calls. It does not require a separate upload, and deployment can be on-premise or hybrid, with meeting-bot and bot-free options.
Security awareness training teaches people what to look for and what procedures to follow. Diopter, on the other hand, looks at the entire conversation during the call and scores it on its four capabilities. Employee training and other security techniques remain important; Diopter can act as a useful additional detection layer to combat social engineering attacks.
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Walk an attack arc with Diopter.
In 30 minutes, we will replay a real deepfake incident, show the signals Diopter would score, and map the verdict your team could act on.