Synthetic content labelling and provenance marking
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.
What this label means
- Verified
- — a named reviewer opened the official source and confirmed the record; the date and the reviewer are shown.
- Pending review
- — linked to the official source and in the queue for a reviewer to confirm.
- Source-linked
- — linked to the official source, not yet confirmed by a reviewer.
On this page 6 sections
- Duties satisfied
- 5
- done properly, does the work
- Duties supported
- 0
- contributes; the duty needs more
- Jurisdictions
- 3
- Evidence items
- 3
In brief
Synthetic content labelling and provenance marking is a technical measure, owned by the engineering lead and run continuously. 5 recorded duties in 3 jurisdictions rely on it, and it satisfies 5 of them outright. It produces 3 kinds of evidence an auditor can ask for.
How is it implemented?
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.
Which legal duties does it serve?
Satisfies means the control, operated properly, does the work the duty asks for. Supports means it contributes but the duty needs more. The official text decides; open it before relying on either.
European Union 3 duties
-
satisfies Legal requirement confidence highProviders of generative AI must mark synthetic output as artificially generated in a machine-readable way
EU AI Act · Article 50(2) · applies from 2 Aug 2026
Machine-readable marking with robustness evidence.
-
satisfies Legal requirement confidence highDeployers must disclose deepfakes and AI-generated text published on matters of public interest
EU AI Act · Article 50(4) · applies from 2 Aug 2026
User-facing labels on deepfakes and public-interest text.
-
satisfies Legal requirement confidence highDisclose AI interaction and label synthetic content
EU AI Act · Article 50 · applies from 2 Aug 2026
Machine-readable marking of synthetic output and deepfake labels.
Singapore 1 duty
-
satisfies VoluntaryReport incidents and mark AI-generated content (generative AI framework)
Singapore Model AI Governance Framework · Generative AI framework, dimensions on incident reporting and content provenance
Watermarking and cryptographic provenance for generated media.
South Korea 1 duty
-
satisfies Legal requirement confidence highAI business operators must label generative AI output and clearly flag realistic synthetic media
AI Basic Act · Article 31(2) and 31(3) · applies from 22 Jan 2026
Labels and provenance marks on generated output and realistic synthetic media.
What evidence shows it is operating?
| Evidence | Type | What it shows |
|---|---|---|
| Content labelling and provenance standard | Procedure or standard operating process | Methods used per modality, label wording and exceptions. |
| Watermark and provenance robustness test | Evaluation or test report | |
| Visible AI-generated content label | Disclosure or notice |
Owner: Engineering lead. Frequency: continuous.
Which risks does it address?
Subdomains of the MIT AI Risk Repository, with the incidents the AI Incident Database has recorded under each. Counts are live; they say how often a risk has materialised, not how well this control prevents it.
- 3.1 False or misleading information Misinformation192 incidents · 53 risk entries
- 3.2 Pollution of information ecosystem and loss of consensus reality Misinformation4 incidents · 22 risk entries
- 4.1 Disinformation, surveillance, and influence at scale Malicious actors138 incidents · 84 risk entries
- 4.3 Fraud, scams, and targeted manipulation Malicious actors412 incidents · 77 risk entries
Which standards clauses does it correspond to?
Clause numbers only. A reference means the standard asks for overlapping work, so evidence may be reusable; it never means the standard discharges a legal duty.
| Framework | Reference | Note | Confidence |
|---|---|---|---|
| ISO/IEC 42001 | Annex A.8.2, A.9.3 | low | |
| NIST AI RMF | MEASURE 2.8; MANAGE 4.1 | Transparency measures for generative AI outputs. | medium |
| OWASP Top 10 for LLM Applications | LLM09 Misinformation | medium |
Cite this record
AIPolicyTracker (2026). “Synthetic content labelling and provenance marking” [AIPT-CTL-0023, verified 26 September 2026 by Bhaskar Bhatt]. https://aipolicytracker.org/id/AIPT-CTL-0023 (accessed 8 November 2026). Data licensed CC BY 4.0.
Permanent id AIPT-CTL-0023: it never changes or gets reused, and its address keeps working if this page moves.
BibTeX
@misc{aipolicytracker2026_syntheticcontentlabellin,
author = {{AIPolicyTracker}},
title = {{Synthetic content labelling and provenance marking}},
year = {2026},
howpublished = {\url{https://aipolicytracker.org/controls/synthetic-content-labelling-and-provenance}},
url = {https://aipolicytracker.org/controls/synthetic-content-labelling-and-provenance},
urldate = {2026-11-08},
note = {Data licensed CC BY 4.0. Accessed 8 November 2026}
}
Disclaimer: informational only, not legal advice. Verify every claim against the linked official sources and consult a qualified lawyer before acting.
Frequently asked questions
Which legal duties does "Synthetic content labelling and provenance marking" satisfy?
It is recorded as satisfying 5 and supporting 0 duties across European Union, Singapore and South Korea. A mapping means the control, operated properly, does the work the duty asks for; the official text decides whether it is enough.
What evidence shows this control is operating?
Content labelling and provenance standard, Watermark and provenance robustness test and Visible AI-generated content label. Owner: Engineering lead. Frequency: continuous.