Synthetic Media Victim Abuse-Response and Disclosure Standard
Added October 2026
Operate a victim-facing response standard for AI-generated likenesses: a takedown workflow with jurisdiction-specific response timelines, a consent register for voice, image, and video use, and an evidence-preservation and law-enforcement handoff path for intimate-image abuse.
Objective
Give people harmed by non-consensual AI-generated likenesses of themselves a fast, documented path to a takedown and redress, instead of treating every report as a one-off.
Maturity Levels
Initial
Reports of non-consensual AI-generated likenesses are handled case by case by whichever team first receives them. There is no standard timeline, no consent record to check against, and no evidence-preservation step.
Developing
A takedown process exists for clear policy violations, but it has no jurisdiction-specific response-time targets, no systematic consent register, and intimate-image cases are not handled differently from ordinary content complaints.
Defined
A written standard sets takedown response-time targets by jurisdiction, maintains a consent register for approved voice/image/video use, and routes suspected intimate-image abuse through an evidence-preservation and law-enforcement handoff path.
Managed
Response times against the targets are tracked and reported. The consent register is checked automatically before a likeness-generating request is served. Escalation and handoff outcomes are reviewed for every intimate-image case.
Optimizing
The standard is tested against simulated abuse reports on a schedule. Findings from handled cases feed back into the systems that generate likenesses in the first place, tightening who can request a given real person's likeness before an abuse report is even filed.
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Evidence Requirements
What an auditor or assessor would expect to see for this control.
- —Written takedown standard with jurisdiction-specific response-time targets by content type
- —Consent register showing recorded approvals for voice, image, or video use, and records of requests checked against it
- —Documented intimate-image abuse path distinct from general content complaints, including evidence-preservation steps
- —Privacy impact assessments for each system capable of generating a real person's likeness
- —Response-time performance against targets for the past 12 months, and law-enforcement handoff records where applicable
Implementation Notes
Why this is broader than payment deepfake defense
SEC-006 defends payment and approval workflows against deepfake impersonation; it exists to stop fraud against the organization. This control exists for a different harm entirely: a real person whose voice, image, or likeness was generated without consent, who is not trying to defraud anyone and is not an employee the organization is protecting itself from, but a victim the organization may have a hand in harming, whether as the platform that generated the content, hosted it, or failed to act on a report about it. The ICO's formal investigation into Grok over harmful synthetic images and the ongoing training-data and likeness litigation against several labs both turn on exactly this question: did the organization have a working response standard, or did it improvise after the fact?
What the standard covers
- Jurisdiction-specific takedown timelines. Response-time targets vary by jurisdiction and content type (for example, intimate-image abuse typically demands a faster target than a general likeness complaint). Document the targets per jurisdiction, not one global number.
- Consent register. Maintain a record of who has consented to have their voice, image, or video used or generated, for what purpose, and for how long. Check requests to generate a specific real person's likeness against this register before fulfilling them, not only after a complaint.
- Intimate-image abuse path. Route suspected non-consensual intimate imagery through a distinct, faster path: immediate removal pending review (rather than queued like ordinary content), evidence preservation sufficient for a law-enforcement referral, and a named contact for the reporting person so they are not resubmitting the same report to a general support queue.
- Privacy impact assessment template. Any system capable of generating a likeness of a real, identifiable person should have a PIA specific to that capability, covering consent capture, misuse scenarios, and takedown mechanics, completed before launch rather than retrofitted after an incident.
- Disclosure. Where a jurisdiction requires it, disclose publicly how victims can report abuse and what response times they can expect; several 2026 regulatory actions specifically examined whether this information was discoverable at all.
Evidence preservation, done right
Preserve enough to support a law-enforcement referral: the generated content itself, generation metadata (prompt, model version, timestamp, requesting account) where available, and the chain of custody from report to removal. Preserving this is not optional even after the content is taken down; removal and evidence preservation are not substitutes for each other.
Example Implementation
Consumer AI platform offering voice and image generation features used by millions of users
Synthetic Media Abuse Report Log (extract)
| Report ID | Type | Jurisdiction target | Actual response | Consent on file? | Outcome |
|---|---|---|---|---|---|
| SM-2026-0914 | Non-consensual intimate image | 4 hours | 2 hours 15 min | No | Removed, evidence preserved, law-enforcement referral offered |
| SM-2026-0918 | Voice clone, impersonation complaint | 48 hours | 31 hours | No | Removed, account suspended |
| SM-2026-0922 | Likeness use, licensed voice actor | 48 hours | N/A | Yes, licensed 2026-06-01 | No action, consent confirmed |
Finding: the third case closed in minutes because the consent register caught it before a takedown review was even needed. The platform is extending consent-register checks to run automatically at generation time, not only at complaint time.