The Spanish Data Protection Authority's January 2026 notice and the European Commission's draft transparency Code signal a regulatory shift: AI-generated content isn't a future concern anymore. With Article 50 of the EU AI Act enforceable from August 2026, you're working against a defined deadline. This checklist translates those obligations into auditable steps your team can execute now.
What This Checklist Covers
This checklist addresses two parallel compliance tracks: treating AI image processing as personal data handling under the General Data Protection Regulation, and preparing for the AI Act's mandatory transparency requirements. You'll implement controls that satisfy both frameworks while building documentation your auditors will accept.
Prerequisites
Before you start, confirm you have:
- Inventory of AI tools currently processing images, videos, or biometric data (internal tools, third-party APIs, employee-facing applications)
- Access to vendor contracts for external AI providers
- Authority to update data processing records under Article 30 of the General Data Protection Regulation
- Sign-off from legal to review intellectual property and likeness rights alongside privacy obligations
If you're missing any of these, pause here. You can't assess risk without knowing what systems you're running.
Checklist Items
1. Document every AI tool that touches images as a personal data processing activity.
Update your Article 30 register to include the AI system name, processing purpose, categories of personal data (facial images, body characteristics, metadata), and legal basis. Each AI tool should have its own entry with a named data controller and processor relationship mapped.
2. Establish a valid legal basis before uploading any individual's image into an AI system.
Review whether you're relying on consent, legitimate interest, contract performance, or another Article 6 basis. If it's legitimate interest, document your balancing test. A one-page legal basis justification per use case, signed by your Data Protection Officer, is ideal.
3. Implement access controls so only authorized personnel can upload images to AI tools.
Apply Role-Based Access Control and the Principle of Least Privilege. Log who uploads what, when. Access logs should be retrievable within 24 hours, showing user ID, timestamp, and file identifier.
4. Assess whether your AI vendor retains copies of uploaded images beyond processing.
Request written confirmation of data retention periods and deletion practices. If the vendor can't guarantee deletion, that's a red flag for your risk register. Look for a vendor attestation stating maximum retention periods and deletion method (overwrite, cryptographic erasure, physical destruction).
5. Map metadata generation risks for every AI output.
Determine whether the AI system embeds EXIF data, watermarks, or identifiers that could enable re-identification. Technical documentation should show what metadata fields the system writes and whether you can disable them.
6. Create a process to evaluate reputational and intellectual property risks before generating content.
Don't limit your review to privacy. Ask: could this output be mistaken for someone's endorsement? Does it use copyrighted material? A three-question pre-generation checklist covering likeness rights, copyright, and potential misattribution is effective.
7. Draft a Data Subject Access Request response procedure specific to AI-generated content.
Data subjects can request copies of their images and details of AI processing. Document how you'll retrieve source files and processing logs. A workflow diagram showing request intake, file retrieval from AI vendor, and response assembly within 30 days is recommended.
8. Classify your AI outputs as fully AI-generated or AI-assisted.
The draft Code distinguishes between content created entirely by AI and content where AI substantially influenced the output. Make this determination per use case. A decision matrix listing your content types (marketing images, training videos, internal mockups) with a classification for each is useful.
9. Prepare to label AI-generated content with a consistent disclosure icon.
Until the official EU icon is adopted, use a two-letter acronym ("AI", "IA", "KI") in a visible location. For deepfake videos, plan for continuous on-screen indicators. Design templates with icon placement documented for each content format you produce are necessary.
10. Document your labeling practices in writing.
Record which content types require labeling, where the icon appears, and who's responsible for applying it. This becomes your compliance evidence. A two-page labeling standard with screenshots showing correct icon placement is ideal.
11. Train staff on when and how to apply AI content disclosures.
Don't assume people will figure it out. Run a 30-minute session covering the classification system, icon usage, and what counts as "substantial influence." Training attendance records and a post-training quiz with a pass threshold are recommended.
12. Establish a monitoring procedure to catch mislabeled content.
Assign someone to spot-check published materials quarterly. If you're publishing at scale, automate detection where possible. A quarterly audit report listing content reviewed, errors found, and correction timeline is effective.
13. Create a channel for reporting mislabeling.
Give employees and external parties a way to flag unlabeled or incorrectly labeled AI content. A dedicated email alias monitored daily, with a documented escalation path to your legal team, is recommended.
14. Define correction timelines for reported labeling errors.
Commit to fixing mislabeling within a defined window (48 hours for public-facing content is defensible). A service level agreement stating response time and correction deadline by content type is necessary.
15. Conduct a cross-functional risk assessment covering privacy, intellectual property, and reputational concerns.
Bring together privacy, legal, and communications teams to evaluate AI tools holistically. Don't silo the analysis. A risk register with columns for privacy impact, IP risk, reputational risk, and mitigation owner is useful.
Common Mistakes
Assuming internal-only content doesn't count. The Spanish DPA's notice is explicit: uploading an image into an AI tool triggers General Data Protection Regulation obligations even if you never share the output. Your Article 30 register and legal basis apply regardless of distribution.
Treating transparency as a marketing decision. Labeling AI content isn't optional branding. It's a regulatory requirement under Article 50 of the AI Act from August 2026. If you're waiting for your communications team to approve icon designs, you're solving the wrong problem.
Skipping vendor due diligence. If your AI provider can't confirm data deletion practices or retention periods, you're accepting unknown risk. You're still the data controller. Their gaps become your audit findings.
Confusing "AI-assisted" with "minor edits." The draft Code defines AI-assisted content as outputs where AI substantially influenced the result. A filter that adjusts brightness probably doesn't qualify. A tool that regenerates facial features does. Document your reasoning.
Next Steps
Start with items 1, 2, and 5. You need to know what systems you're running, establish legal grounds, and understand metadata risks before you can implement transparency controls. Block two hours this week to update your Article 30 register and request vendor attestations.
By June 2026, the European Commission will finalize the transparency Code. By August 2026, Article 50 obligations are enforceable. If you're starting now, you have runway. If you're waiting for final guidance, you'll be implementing under deadline pressure. Your auditors will ask what you did between January and August 2026. Make sure you have documentation showing proactive compliance, not reactive scrambling.
