I’ve been shipping AI-generated imagery into client projects and marketing campaigns for a while now, and one thing became obvious: getting those assets through brand compliance and legal spot checks isn’t automatic. It takes process, provenance, and a little bit of foresight. Below is a practical, hands‑on checklist I use to move AI imagery from a creative idea to an approved, production-ready asset.

Understand your risk profile up front

Before you generate a single image, ask yourself where this asset will live and what it will do. Will it appear in a paid ad, a product page, or an internal moodboard? The higher the visibility and commercial intent, the stricter the checks need to be.

  • High risk: paid ads, packaging, investor-facing materials, product UI
  • Medium risk: website hero images, social posts, email headers
  • Low risk: internal moodboards, rough comps, exploratory art

    • Label each asset with its risk level in your brief so reviewers know what standard to apply.

    Collect and record provenance metadata

    One of the fastest ways to reassure legal and brand teams is to provide clear provenance. I keep a simple metadata record for every AI image I create.

    Field Example / Notes
    Generator tool Midjourney v6, DALL·E 3, Stable Diffusion XL
    Prompt Full prompt text and any negative prompts or seed values
    Model license Link to provider TOS and model-specific license (commercial use allowed?)
    Source material Any uploaded images, sketches, or reference photos (with rights info)
    Generation date Timestamp of image creation
    Creator Name of the person who generated / edited the asset
    Post‑processing Software and steps used (eg. Photoshop: color match, retouch)

    I store that data in a shared drive or a lightweight asset management sheet so reviewers can quickly scan the why and how behind an image.

    Prompts, seeds and the “why” — be transparent

    I always include the full prompt verbatim. Why? Because prompts may contain references to visual styles, trademarks, or public figures that trigger brand or legal flags. Including the prompt lets reviewers identify risky phrases quickly.

  • Include seed numbers when available — they can help reproduce a result.
  • Include any iterations or refactorings of the prompt so there’s an audit trail.

    • Check for trademark, copyright and likeness issues

    This is where legal teams get nervous. I run three straightforward checks before sending anything for legal review:

  • Trademark scan: visually inspect for logos, product markings, or distinctive brand elements (Nike swoosh, Coca‑Cola script). If the generator added anything resembling a known mark, either remove or recreate it following licensing rules.
  • Copyrighted works: ensure the prompt didn’t request or resemble a specific copyrighted artwork or copyrighted character. If a prompt asked for “a paint ing in the style of Picasso,” legal teams may require alternative wording like “inspired by Cubist composition” to avoid style‑of disputes.
  • Likeness of real people: never generate and publish imagery that uses a recognizable person’s likeness without explicit consent. This includes celebrities, public figures, or employees if they can be identified.

    • For high-risk outputs, I run a simple image similarity check against known logos and public figure photos. Tools like TinEye, Google Images, or internal IP databases can help flag issues fast.

    Brand compliance: match the brand voice and visual system

    Brand teams want consistency. I translate brand guidelines into concrete checkpoints and score each AI image against them:

  • Color palette: are primary/secondary brand colors respected? Use a color-picker and note delta values if tweaks were made.
  • Typography and layout: if the image includes type or signage, ensure fonts are licensed and align with brand typography rules.
  • Tone and messaging: is the imagery appropriate for the brand voice? (e.g., playful vs. premium)
  • Accessibility: contrast checks and alt text drafts—always deliver suggested alt text with the asset.

    • I’ll include a short “brand compliance note” with each asset: 2–4 bullet points stating what’s aligned and what required fixes.

    Document licensing and provider terms

    Different generators and models have different licensing terms. I attach or link to the exact terms used when the image was created and note any restrictions on commercial use, attribution, or model weights.

  • If you used a SaaS generator (Midjourney, DALL·E, Adobe Firefly), link the subscription agreement and confirm commercial use rights.
  • If you used an open‑source model (Stable Diffusion variants), note the model card and any dataset restrictions (LAION‑derived data controversies, for example).
  • Note any third‑party assets (textures, overlays, stock photos) and include their licenses.

    • Build a quick legal spot‑check checklist

    I give legal teams a one‑page spot check that they can run in under five minutes. It looks like this:

    Spot Check Item Pass / Fail Notes
    Model license allows commercial use Link to TOS
    No obvious trademarks or branded elements List elements checked
    No recognizable likenesses without releases Names/IDs
    No blatant copy of a copyrighted artwork Reference image check
    Attribution / credits added if required Exact text

    This format lets legal sign off quickly or flag a specific area that needs attention.

    Quality control and human review — don’t skip it

    AI can introduce subtle artifacts or uncanny details that break trust with users. I run a visual QC pass focused on:

  • Facial details and anatomy (eyes, teeth, hands are common trouble spots)
  • Text legibility and context (AI often produces believable but unreadable text)
  • Semantic coherence (does the object casting a shadow match the light source?)

    • For production assets, I also run them through the same QA pipeline as other creative assets—color-correcting, exporting to correct formats, and embedding metadata. If the image will be printed, I generate a CMYK proof and check embossing or die-cut interactions where relevant.

    Attribution and transparency — practical wording

    Some platforms and teams want transparency about AI assistance. I keep a short set of approved attribution lines designers can use depending on policy:

  • “Image created with assistance from [tool]”
  • “Generative image (prompted and edited by [designer])”
  • “Contains AI-generated elements — see provenance”

    • Place the attribution where the audience will see it: in the credits section of a webpage, the campaign’s legal footer, or in the image metadata.

    Retention, audits and incident response

    Retention matters. Keep a copy of the prompt, the generated image, and the final edited asset for at least as long as the campaign runs (I recommend 2–3 years for marketing materials). If a complaint arises later, you’ll be able to reconstruct the creative process quickly.

  • Store a zipped folder: original prompt, intermediate outputs, final files, and the metadata table.
  • Set up an incident response plan: whom to notify (legal, brand, communications), timeline for responses, and a templated public statement if a takedown or correction is needed.

    • Having these artifacts and a clear process turns a potential compliance headache into a manageable, auditable workflow.

    Tools and integrations I recommend

    I use a few tools to automate and document parts of this workflow:

  • Notion or Google Sheets: lightweight asset provenance tracker.
  • ExifTool: embed custom metadata directly into image files (prompt, generator, creator).
  • TinEye / Google Images: reverse image search for similarity checks.
  • Adobe Bridge or an MAM (Media Asset Management) for larger teams to enforce metadata standards.

    • Integrate these into your existing DAM or project management system so reviewers have a single place to go.

    If you start with these steps—clear risk classification, detailed provenance, license checks, fast legal spot checks, and a human QC pass—you’ll dramatically improve the chance an AI image sails through brand and legal reviews. The rest comes down to discipline: document everything, be transparent, and iterate the process as regulations and model terms evolve.