A familiar voice, message, account, or request can bypass ordinary caution when verification is informal.
Deepfake-Aware Verification
Verify High-Risk Requests Without Trusting a Voice or Video Alone
Reduce reliance on how convincing a caller, video, or message appears by requiring independent proof for sensitive actions.
Why this matters
Security fails at the handoff between people, systems, identity, and high-impact decisions.
One tool cannot solve impersonation, compromised access, payment changes, recovery, or vendor risk. The workflow needs layered controls people can actually follow.
Accounts, devices, vendors, integrations, and former staff retain permissions without clear review.
Backups, escalation, evidence, customer communication, and decision authority fail when the first real incident arrives.
How the solution works
Map the highest-risk workflow, then layer prevention, verification, and recovery.
Client-owned assetsHuman approval gatesInspectable handoffNo outcome guarantees
Identify assets, actors, access, approvals, failure modes, and realistic impact.
Apply practical controls to identity, devices, data, payments, vendors, and change requests.
Document escalation and recovery, train the team, and test the controls under realistic pressure.
Fit & boundaries
Know what the engagement does—and does not—include
Clear ownership and exclusions protect the result before work begins.
Good fit when
- Staff act on payment, payroll, credential, customer-data, or executive requests.
- Voice or video familiarity is currently treated as identity proof.
- The business can enforce a verification step even when a request feels urgent.
Launch boundaries
- No promise to detect every synthetic voice, image, or video.
- No single detector is treated as decisive identity proof.
- No biometric collection or surveillance added without necessity, legal review, and explicit governance.
What Custom AI Works does
A working system, not a vague promise
Layered identity and transaction verification workflows designed for AI-enabled impersonation, voice cloning, compromised accounts, and social engineering.
Out-of-band checks
Verify through a known channel instead of contact details supplied in the request.
Shared procedures
Use pre-agreed phrases, approval chains, and role-based thresholds appropriately.
Safe delay
Add hold-and-review rules for unusual payment, access, payroll, or data requests.
Decision record
Document request, evidence, approvers, exceptions, and escalation without over-collecting data.
Packages
Start at the level your operation needs
No public dollar claims: scope, access, third-party costs, and owner responsibilities are confirmed first.
Starter
Scope and pricing are confirmed after discovery. No media spend or third-party fees are hidden in the quote.
- Current-state audit
- Prioritized action plan
- One bounded implementation
- Owner handoff and next steps
Growth
Scope and pricing are confirmed after discovery. No media spend or third-party fees are hidden in the quote.
- Everything in Starter
- Managed execution cadence
- Reporting and decision log
- Monthly optimization review
Custom
Scope and pricing are confirmed after discovery. No media spend or third-party fees are hidden in the quote.
- Cross-platform scope
- Custom integrations or workflows
- Governance and approval design
- Phased rollout and runbooks
Delivery
A four-step path with approval gates
Map high-consequence requests
A named owner approves the output before the next material step.
Design independent verification steps
A named owner approves the output before the next material step.
Test realistic scenarios and exceptions
A named owner approves the output before the next material step.
Train staff and review incidents
A named owner approves the output before the next material step.
Evidence
Artifacts you can inspect before case-study claims
Proof before promises
Until a privacy-safe client aggregate qualifies, this service is demonstrated with a sanitized audit, a sample operating checklist, a synthetic-data reporting view, and the working process used to deliver the engagement.
Primary guidance: FTC approaches to AI-enabled voice cloning · NIST AI Risk Management Framework
Client results may be published only as anonymous aggregates from at least five authorized clients or campaigns, with the sample, period, metric definition, and verification date disclosed. Public examples remain clearly labeled as external.
FAQ
Questions, answered
Can software detect every deepfake?
No. Detection tools can be useful signals, but they can produce false results and can be evaded. The workflow relies on multiple independent controls.
What should trigger extra verification?
Examples include changed payment instructions, urgent secrecy, new contact details, credential resets, payroll changes, data exports, and unusual executive requests.
Do we need biometrics?
Usually not as a first step. Known-channel callbacks, approvals, transaction limits, and shared procedures often reduce risk with less privacy impact.
Can you train our team?
Yes. Training uses approved scenarios, clear stop rules, and an escalation path that staff can follow under pressure.
Related
Build the rest of your Build & Secure system
Make the next decision
Where can trust fail in a high-impact workflow?
Describe the people, systems, approvals, and failure scenario. We’ll identify the right assessment or verification starting point without pretending one control solves everything.
- Which people, accounts, devices, data, or approvals are exposed?
- What impersonation, access, payment, or recovery failure matters most?
- Which verification, logging, escalation, and recovery controls must work together?