AI Rights & IP Dashboard

Regulatory and platform intelligence for ethical, rights-clean generative AI in creative work.

A living dashboard of the laws, court rulings, platform terms, and watermarking mandates that govern AI-generated images and video. Built for creative directors, producers, and legal counsel who need to ship work without inheriting risk.

Platform rights matrix Regulation tracker
01 — Platform rights matrix

What each platform lets you own, license, and defend.

Verdicts derived from current terms of service, indemnification scope, training data disclosures, and active litigation. Sort any column. Click a row to expand.

Platform Type Verdict Commercial Indemnity Training data Provenance Litigation
02 — Regulation tracker

What the law says, where, and when it bites.

Every statute, agency rule, executive action, and major court ruling we track — US, EU, UK, and select international. Filter by jurisdiction, topic, or status.

03 — Watermarking & provenance

The standards and the statutes making them mandatory.

C2PA is the default provenance layer; SynthID is Google's invisible watermark. The EU AI Act, California's SB 942, and China's GB 45438-2025 are making one or both mandatory in production.

Standards in use

Mandates by jurisdiction

04 — Handle with care

Platforms under active fire — and the ones to lean on.

Consolidated from litigation dockets, regulatory actions, and ToS red flags. The "favor" list is where indemnification actually backs the output.

Red flags

Platforms to favor

05 — TRACE Framework

Traceable & Responsible AI Creative Ethics.

A governance framework applied to every AI-touched deliverable — so every asset is traceable, ethical, and defensible.

AI Governance Framework v1.0

Brands face real consequences — legal exposure, reputational damage, and loss of client trust — when AI usage is undisclosed, data is mishandled, or content is misleading. TRACE operationalises a commitment to integrity, ensuring every asset delivered is one that can stand behind scrutiny.

  • T
  • R
  • A
  • C
  • E

Why TRACE exists

  • Reduced legal & reputational risk Documented AI usage creates a clear audit trail.
  • Stronger client trust Clients publish with confidence knowing ethical QA has been performed.
  • Competitive positioning TRACE signals maturity in a market where most agencies have no AI governance.
  • Talent confidence Team members work within clear, ethical boundaries.

The five pillars

T

Traceable Assets

Every asset carries a documented origin — source files, prompts, model versions, and licenses logged in the TRACE Sheet at kickoff and confirmed at delivery.

R

Responsible AI Use

Models are chosen for licensing posture, indemnification, and provenance support — cross-checked against the platform rights matrix on this dashboard before use.

A

Accountable Claims

Any factual claim in a deliverable — copy, chart, quote, statistic — is verified against a primary source before it ships. Speculation is labeled as such.

C

Compliant Data

Prompts, references, and training inputs respect GDPR, CCPA, and client-specific data rules. Personal or confidential data never enters an unapproved model.

E

Ethical QA

Independent sign-off before delivery. A four-eyes minimum applies: the QA reviewer is never the primary creator of the work being reviewed.

E

Ethical QA checklist

Independent sign-off before delivery. Four-eyes minimum.

  • All assets have confirmed licensing for their intended use.
  • AI usage has been declared in the TRACE Sheet.
  • All factual claims verified against a primary source.
  • Sensitive content categories have received appropriate specialist review.
  • Data sources documented and compliant.
  • Client-specific ethical guidelines (if any) applied.
  • Final asset matches the approved version in the TRACE Sheet.
  • QA reviewer was not the primary creator of the work.

Regulatory context

EU AI Act 2024
Transparency requirements for AI-generated content; disclosure obligations for high-risk use cases.
Digital Authenticity & Provenance Act 2025
Organisations must be transparent about digital content verification and provenance practices.
FTC guidance US
Disclosure required when AI generates endorsements, testimonials, or product claims.
GDPR / CCPA
Applies to personal data used in AI prompts or training pipelines.

Maturity model

  1. 01

    Aware

    Team is familiar with TRACE. Sheets completed retroactively. No consistent workflow integration.

  2. 02

    Practicing

    Sheets completed at project start. AI usage declared. Basic ethical QA performed at delivery.

  3. 03

    Integrated

    TRACE embedded in kickoff and delivery checklists. All pillars actively applied. Incident log maintained.

  4. 04

    Optimised

    TRACE metrics tracked per engagement. Client-facing reporting available. Framework iterated based on findings.

Roles & responsibilities

TRACE Framework owner
Maintains and evolves the framework. Reviews incident logs. Approves exceptions. Reports to leadership.
Project lead
Initiates the TRACE Sheet at kickoff. Identifies risks. Escalates concerns to the framework owner.
Creative team
Logs AI usage and asset origins in real time. Raises concerns immediately. Completes TRACE Sheet sections.
QA reviewer
Performs independent Ethical QA review. Signs off the TRACE Sheet. Must not be the primary creator of reviewed work.
All staff
Understand and apply TRACE principles. Complete annual training. Report concerns without fear of reprisal.