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Resource Licensing

Generated Asset Provenance Record

AI-generated assets have provenance too: tool, model, prompt, source references, edit history, rights risks, and reuse boundaries must be recorded together.

generated-assetsprovenancelicenserag

Design Principle

An asset library cannot treat AI generated as a license or source explanation. Reuse depends on input rights, depicted subject, edit chain, verification signals, and usage context.

Large Theory

Generated assets are not source-less assets. They may depend on reference images, branded objects, likeness, artist style, real places, or product appearance. When future agents retrieve assets, they cannot safely reuse an image if they do not know how it was generated, what inputs it used, and which rights risks apply.

Small Points

  1. Intake fields include at least tool, model/version, createdAt, promptSummary, sourceReferences, inputRights, editHistory, assetPath, usageRoles, altText, commercialUse, and ragKeywords.
  2. People, celebrities, brands, trademarks, product appearance, artist style, real places, and news events need separate risk flags.
  3. Upscaling, cropping, repainting, background replacement, and compositing are edit history and cannot be lost.
  4. C2PA, Content Credentials, or watermarking are provenance signals, not proof that an image is accurate, unedited, legally owned, or used in the right context.
  5. If the generated image is a mockup, filename, alt text, and description should say mockup/generated instead of presenting it as a real product screenshot.

Design Judgment

A generated asset record must answer who or what generated it, what inputs it used, what it depicts, whether it is commercial-safe, whether it can be reused across projects, and which risk limits apply. If it cannot, agents should not recommend it automatically.

Implementation Notes

Create dedicated provenance JSON or catalog fields for generated assets. RAG keywords should include governance terms such as generated, mockup, safe-for-commercial, needs-review, brand-risk, and likeness-risk in addition to style and use terms. Preserve sidecar metadata when exporting or compressing assets.

Counterexamples/Risks

Only writing AI generated, using a brand website screenshot as reference without rights notes, creating likeness risk from a real-person reference, losing Content Credentials after CDN upload, and treating a generated UI mockup as real case evidence.

Case Study

C2PA defines Content Credentials as an open technical standard for establishing content origin and edit history. The Content Authenticity Initiative emphasizes recording creation, editing, and generative AI information across the content lifecycle. OpenAI also notes that C2PA and SynthID are provenance signals and do not prove an image is accurate, unedited, legally owned, or in the correct context. The reusable lesson is to record both the signal and its limits.

Source Links

C2PA: https://c2pa.org/ Content Authenticity Initiative: https://contentauthenticity.org/how-it-works OpenAI C2PA and SynthID: https://help.openai.com/en/articles/8912793-c2pa-and-synthid-in-openai-generated-images

Agent Directive

Before intaking generated assets, record provenance and risk fields; without generation chain, input rights, or risk flags, do not recommend the asset as reusable.