{"slug":"synthetic-content-labelling-and-provenance","id":"AIPT-CTL-0023","title":"Synthetic content labelling and provenance marking","kind":"technical","kind_label":"Technical measure","purpose":"Makes AI-generated or manipulated audio, image, video and text identifiable as such, both to people looking at it and to software checking it, so that it cannot easily be passed off as authentic.\n","description":"Generation pipelines attach a machine-readable provenance signal to output, for example a content credential manifest, an invisible watermark or metadata, using a method that survives the common ways the content will be shared. Where the audience needs to see it, a visible label or spoken notice is added as well. The team documents which methods are used for which modality, tests detectability and robustness to editing, and keeps a verification tool or endpoint available. Downstream products that display deepfakes or AI-written material of public interest apply the label rules set by the organisation's editorial policy.\n","owner_role":"Engineering lead","frequency":"continuous","frequency_label":"Continuous","risk_subdomains":["3.1","3.2","4.1","4.3"],"evidence":[{"type":"procedure","title":"Content labelling and provenance standard","description":"Methods used per modality, label wording and exceptions."},{"type":"evaluation_report","title":"Watermark and provenance robustness test","description":null},{"type":"disclosure_notice","title":"Visible AI-generated content label","description":null}],"framework_references":[{"framework":"iso_42001","framework_name":"ISO/IEC 42001:2023","reference":"Annex A.8.2, A.9.3","note":null,"confidence_level":"low"},{"framework":"nist_ai_rmf","framework_name":"NIST AI RMF 1.0","reference":"MEASURE 2.8; MANAGE 4.1","note":"Transparency measures for generative AI outputs.","confidence_level":"medium"},{"framework":"owasp_llm_top10","framework_name":"OWASP Top 10 for LLM Applications","reference":"LLM09 Misinformation","note":null,"confidence_level":"medium"}],"obligations":[{"slug":"south-korea-ai-basic-act-art-31-generative-output-labelling-and-deepfake-notice","title":"AI business operators must label generative AI output and clearly flag realistic synthetic media","relationship":"satisfies","note":"Labels and provenance marks on generated output and realistic synthetic media.","confidence_level":"high","is_binding":true,"policy":"south-korea-framework-act-on-the-development-of-artificial-intelligence-and-establishment-of-a-foundation-for","jurisdiction":"south-korea","url":"https://aipolicytracker.org/obligations/south-korea-ai-basic-act-art-31-generative-output-labelling-and-deepfake-notice"},{"slug":"singapore-mgf-genai-incident-reporting-and-provenance","title":"Report incidents and mark AI-generated content (generative AI framework)","relationship":"satisfies","note":"Watermarking and cryptographic provenance for generated media.","confidence_level":"medium","is_binding":false,"policy":"singapore-model-ai-governance-framework","jurisdiction":"singapore","url":"https://aipolicytracker.org/obligations/singapore-mgf-genai-incident-reporting-and-provenance"},{"slug":"eu-ai-act-art-50-2-synthetic-content-marking","title":"Providers of generative AI must mark synthetic output as artificially generated in a machine-readable way","relationship":"satisfies","note":"Machine-readable marking with robustness evidence.","confidence_level":"high","is_binding":true,"policy":"eu-ai-act","jurisdiction":"eu","url":"https://aipolicytracker.org/obligations/eu-ai-act-art-50-2-synthetic-content-marking"},{"slug":"eu-ai-act-art-50-4-deepfake-and-public-interest-text-disclosure","title":"Deployers must disclose deepfakes and AI-generated text published on matters of public interest","relationship":"satisfies","note":"User-facing labels on deepfakes and public-interest text.","confidence_level":"high","is_binding":true,"policy":"eu-ai-act","jurisdiction":"eu","url":"https://aipolicytracker.org/obligations/eu-ai-act-art-50-4-deepfake-and-public-interest-text-disclosure"},{"slug":"eu-ai-act-transparency-article-50","title":"Disclose AI interaction and label synthetic content","relationship":"satisfies","note":"Machine-readable marking of synthetic output and deepfake labels.","confidence_level":"high","is_binding":true,"policy":"eu-ai-act","jurisdiction":"eu","url":"https://aipolicytracker.org/obligations/eu-ai-act-transparency-article-50"}],"related_controls":["ai-interaction-and-use-disclosure","genai-red-team-testing"],"review_status":"verified","confidence_level":"medium","last_verified_at":"2026-09-26","reviewed_by":"Bhaskar Bhatt","updated_at":"2026-10-15T06:46:23+00:00","url":"https://aipolicytracker.org/controls/synthetic-content-labelling-and-provenance","context_url":"https://aipolicytracker.org/controls/synthetic-content-labelling-and-provenance.md"}