Keep a clear record of how content was made
A customer asks whether the person in an ad is real. A colleague needs the original photo after an edit. These are ordinary questions about how a piece of content was made. Keeping the source and recording meaningful changes makes them easier to answer. Content Credentials can support that record when your tools preserve them.
Start with four questions: was the image edited, is the person real, do you have the original and who approved the claim? This guide covers the technical standard, platform labels and your own policy. Check current rules for each country and platform before relying on the guidance.
What Content Credentials and C2PA are
C2PA, the Coalition for Content Provenance and Authenticity, publishes an open technical standard for attaching a signed history to a media file. Content Credentials makes that history available to viewers. The record sits inside the file and can show whether someone has altered it.
What a credential can record
- Which tool or camera created the asset.
- Which editing steps were applied afterward, including whether generative AI was involved.
- Who signed the record, so a viewer can decide how much to trust it.
What it cannot do
A credential records a history and who signed it; it does not prove that the picture depicts the truth. Screenshots, exports and uploads that remove metadata can also remove the credential, and many platforms do not yet display it. The record depends on support in each tool used to create or edit the file. Keep your own source files and notes to cover gaps.
What platforms label on their own
Platforms are adding their own layers, and they change often. One concrete example: ByteDance's Seedance 2.0 is integrated into TikTok's Symphony Creative Studio, and content made there gets an automatic AI-disclosure label. In that case the platform handles the labeling for you, which is convenient but only covers assets made inside that tool.
Major social and advertising platforms offer ways to mark content as AI-generated or AI-altered. Some require labels for realistic content, and some read recorded file history where available. Check each platform’s policy before publishing and review it every quarter, since the details change.
When to disclose
Consider whether a viewer could be misled about how the content was made. If so, explain it clearly. Use the categories below as a starting point for your own rules.
Disclose by default
- Realistic people, including AI avatars and synthetic voices presented as if they were real customers or staff.
- Altered product results: before and after images, texture, size, performance or anything a buyer would use to decide.
- Realistic scenes of events that did not happen, or places you did not visit.
- Never invent a customer review or testimonial. An AI label does not turn a fabricated experience into a genuine endorsement.
Usually not needed
Routine touch-ups such as color correction, cropping, background cleanup or resizing rarely change what a viewer believes. Clearly stylized illustration and abstract graphics are also low risk. If you are unsure which side an asset falls on, disclose. The cost is a short line of text.
Avatar and UGC-style ads deserve the most care, because the format invites viewers to assume a real person is speaking. Our article on AI avatars and UGC for brand marketing looks at how to use them without misleading anyone.
Write an internal disclosure policy
A policy can fit on one page. Its main job is to remove case-by-case debate. Here is a version for a fictional bakery, Hearth and Crumb, with three staff and a single social manager.
- Scope: applies to every image, video, voiceover and caption published under the bakery's accounts, including paid ads.
- Always label: any AI-generated person, any generated voice, and any image where the product shown was created or materially altered by AI.
- Never do: present a generated person as a real customer; show a cake, size or decoration we cannot make; imitate a real individual or a competitor's brand.
- Wording: use the platform's built-in AI label where available. Otherwise add a plain line such as "Image created with AI, product photographed in our kitchen."
- Approval: the owner signs off on every AI-made ad before spend begins, and records the date.
- Records: save the source photo, the prompt, the final file and the approval note for each campaign for at least a year.
- Review: reread platform policies and this page every quarter, and update it when a rule changes.
Nothing in this list needs software. A shared folder and a spreadsheet are enough to start.
Write the label in plain words
Keep labels short, specific and easy to find beside the image or video, or in the platform’s label field. Plain wording tells viewers what was changed without technical detail.
- For a generated background: "Background created with AI. Product photographed by us."
- For an avatar presenter: "AI-generated presenter. Not a real customer."
- For a fully generated visual: "Image created with AI."
Describe the actual change. "Enhanced with AI" understates a fully generated scene, while "AI-generated" may confuse viewers when a photograph has only been lightly retouched. Accurate wording helps people understand what they are seeing.
Keep source files and edit history
Keep four things together for each campaign: the original photo or footage, generation settings and prompts, the finished export, and a note of who approved it and when. That makes questions from customers, reviewers or partners easier to answer.
Name files so the story is readable without opening them: spring-loaf-original.jpg, spring-loaf-ai-background-v2.png, spring-loaf-final-1080.png. Keep the layered or project file too, since it shows what was added on top of the generation. Be careful with what you publish, though. Prompts and private customer details belong in your internal record, not in public metadata.
Mistakes that cause trouble later
Most problems come from a few repeat patterns rather than from bad intent.
- Deleting the original photo after generating a variation, then having nothing to show when asked.
- Letting a freelancer publish AI-made assets without telling the account owner.
- Reusing one generated face across many ads until it reads as a fake customer base.
- Assuming the platform's label covers assets made in a different tool.
- Writing the policy once and never opening it again.
A one-page policy and a quarterly fifteen-minute review can prevent these avoidable problems.
Where this fits in your workflow
The habit that makes all of this stick is doing it at the point of creation, not after publishing. In the AI video generator and image tools in SEENALYZE AI, drafts, brand settings and edits live in the same workspace as the final post, so the history of an asset stays close to the asset. Our guide to keeping AI visuals on-brand shows the editing side of the same discipline.
Frequently asked questions
Are Content Credentials required?
They are a voluntary standard, not a universal requirement. Some jurisdictions and platforms are introducing their own labeling rules, so check current guidance for the places you sell and advertise.
Do I need to label AI-edited product photos?
Check the current rules of each platform. If a scene could mislead viewers about how it was made, explain the edit. A label does not make a false product feature or result acceptable; correct or reject that image.
Will a label hurt performance?
We do not have reliable data on that, so test it on your own audience. Honest disclosure protects trust, and a misleading ad costs far more than a label does.
What if a platform strips the credential?
It often will. That is why you keep your own source files, prompts and approval notes as the durable record.
Keep a clear record for each post
Draft, edit and approve AI-made posts in one workspace, keeping source files and decisions with the finished content.
