Deepfakes are synthetic audio, video, or images generated or altered to make a person appear to say or do something they never did. In security, they are used for impersonation, fraud, social engineering, and identity verification bypass, so controls must validate provenance, liveness, and trusted identity signals.
What deepfakes are used for in security
Deepfakes are not just a media integrity problem, they are a practical attack primitive. In security incidents, synthetic audio and video are used to impersonate executives, suppliers, customers, or employees, and to create enough apparent legitimacy to trigger payment, disclosure, or account-change actions.
The main danger is that deepfakes exploit normal human trust in faces, voices, and familiar communication channels. When a forged image or voice note is convincing enough, the attack can bypass suspicion long enough for fraud, social engineering, or identity verification abuse to succeed.
Why deepfakes are effective against verification processes
Deepfakes work because many workflows still treat appearance and voice as strong evidence. If a process assumes that a live video call, voicemail, or recorded clip is inherently trustworthy, an attacker can use synthetic media to imitate a person without controlling the real person’s device or account.
This makes deepfakes especially dangerous in payment approvals, executive requests, HR onboarding, customer support resets, and any workflow where a single convincing interaction can override a weaker control. The problem is not only realism, but timing, urgency, and the pressure to act quickly.
Strong verification should not depend on the media itself. It should rely on corroborating signals such as independent callbacks, known contact paths, prior relationship context, and procedures that do not accept a single audio or video channel as proof of identity.
How organisations reduce deepfake exposure
Exposure usually starts with weak identity checks and overreliance on one channel. Organisations reduce that exposure by using step-up verification, separating approval from request channels, and designing processes that assume audio and video can be forged.
For high-risk events, the safest pattern is to verify through a second trusted path that is already known to the organisation, not one supplied by the requester. That is why deepfake-resistant controls often look like process design, not just technology: they force the requester to prove legitimacy in a way the attacker cannot easily fake.
Where content provenance tooling exists, it can help by preserving origin and edit signals for authentic media. But provenance alone is not enough, because many deepfakes are consumed in live interactions where the stronger control is still human and procedural verification.
Deepfakes, Social Engineering and AI Impersonation Guide covers the main defensive patterns for voice, video, and executive impersonation.
What to investigate after suspected deepfake abuse
If a deepfake succeeds, the investigation should focus on what the attacker obtained, not only on whether the media was synthetic. That can include fraudulent transfers, password or MFA resets, account changes, disclosure of sensitive data, or the creation of new trust relationships that now need review.
Post-incident review should also look for adjacent compromise paths. A deepfake often works best when paired with another weakness, such as a poorly authenticated help desk process, weak approval segregation, or a contact list that can be socially engineered into accepting a false identity.
Arup deepfake fraud 2024 illustrates how synthetic video can be used to drive a high-value payment fraud outcome.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST SP 800-53 Rev 5, NIST SP 800-63 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST SP 800-53 Rev 5 | IA-2 — Identification and Authentication (Organizational Users) | Deepfakes target weak user verification and identity assurance. |
| IA-5 — Authenticator Management | Deepfake abuse often follows compromised credentials or reset flows. | |
| AU-6 — Audit Record Review, Analysis, and Reporting | Deepfake incidents require review of approval, reset, and fraud evidence. | |
| Recommendation — Use IA-2 to require stronger authentication before sensitive approvals or account changes. Apply IA-5 to manage authenticators and reduce reliance on easily impersonated channels. Use AU-6 to analyze logs for abnormal verification and transaction activity. | ||
| NIST SP 800-63 | Digital Identity Guidelines | The term directly affects identity proofing and phishing-resistant verification. |
| Recommendation — Align verification flows with stronger identity assurance and phishing-resistant methods. | ||
| CIS Controls v8 | CIS-6 — Access Control Management | Deepfake attacks often aim to obtain or change access through social engineering. |
| Recommendation — Use CIS-6 to tighten approval paths for sensitive access and account changes. | ||
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on October 23, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org