INSIGHTS
Analysis, white papers, and case studies on creative IP in the age of AI. From the people building the standard.
The legal status of TDM opt-outs in machine-readable form
CIP's position on whether machine-readable TDM opt-outs declared in cip.md meet UK and EU statutory requirements — and the operational implications for rights holders and AI operators.
Read →The status of AI-generated works under UK CDPA s.9(3)
Three competing applications of section 9(3) and CIP's reasoning for the most coherent interpretation — the human-contribution test.
Read →Subsisting rights and registered rights — understanding the difference
One of the most common sources of confusion in AI and intellectual property discussions is the difference between rights that subsist automatically and rights that require registration.
Read →Rights subsistence: what AI companies need to know
Rights subsistence is one of the most important and least understood concepts in the AI-and-IP debate. Understanding it is not optional for AI companies.
Read →Provenance in the AI pipeline
Provenance is the documented history of a creative work. In the age of AI, it has become an operational necessity — a live, machine-readable signal that travels with content through every stage of the pipeline.
Read →Understanding the Core Data Record
The Core Data Record — or CDR — is the provenance infrastructure at the heart of the CIP framework. The structured record that captures what a creative work is, who created it, and on what terms it may be used.
Read →How NILP rights work in the age of AI
Name, Image, Likeness, and Publicity rights — collectively known as NILP — are the rights that protect a person’s ability to control the commercial use of their identity in the age of AI.
Read →What is TDM opt-out and why does it matter?
Text and data mining opt-out is one of the primary legal mechanisms through which rights holders can assert that their content may not be used for AI training without permission.
Read →How AI training data creates hidden copyright liability
Why ingesting copyrighted content into a training corpus does not extinguish the rights subsisting in it; how UK DUA Act 2025, EU AI Act Article 53 and US fair use jurisprudence interact; and what AI companies actually face when content carrying TDM opt-outs enters their pipelines without consent.
Read →How to audit your AI outputs for IP risk — a step-by-step framework
The CIP audit methodology applied to a working AI content pipeline: inventory of ingested content, CDR coverage assessment, output sampling for derivative-rights exposure, NILP screening, and the documentation required to meet Platform Certification audit standards.
Read →The TDM opt-out — what every operator must do now
UK DUA Act 2025, EU AI Act Article 53, four-field minimum declaration, legal weight of machine-readable signals. A three-page briefing for senior decision-makers.
Read →Voice cloning and the NILP Downstream Obligation
Why a brand using AI-generated voice in advertising owes liability to the named voice actor regardless of intermediaries. A two-page briefing.
Read →Generative IP Liability — building an underwriting model for AI content risk
The CARI risk scoring framework, GIPL policy architecture, and the actuarial considerations specific to AI content pipelines — including Training Data Dividend exposure, NILP Downstream Obligation tail risk, and platform compliance correlation.
Read →What "rights-aware ingestion" actually means
The six audit areas, the 95% Rights Payload threshold, and the platform engineering changes required to meet certification. A three-page briefing.
Read →What happens legally when AI transforms copyrighted works?
Derivative rights, moral rights of integrity, and the NILP Downstream Obligation as the three independent legal exposures created when an AI system generates output recognisably derived from a specific creator's work, voice, or likeness.
Read →The Training Data Dividend — paying creators in the AI era
The pay-per-contribution-path model, revenue waterfall through the Rights Registry, collecting society interaction. A three-page briefing.
Read →Subsisting vs registered rights — why this distinction matters in AI
The conceptual foundation of the CIP framework: rights that subsist from the moment of creation versus rights that require formal registration. A two-page briefing.
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