AI Rights Management: rethinking digital rights for the AI era

As artificial intelligence (AI) reshapes content creation and business operations, the limitations of traditional Digital Rights Management (DRM) systems are becoming apparent. This article introduces AI Rights Management (AI-RM) as a novel framework that helps people and companies leverage generative AI while mitigating legal and operational risks.

While DRM adopts a restrictive approach to the use and distribution of digital content, AI-RM is mainly concerned with the provision of technical and legal safeguards to ensure the rightful use of AI. In the context of generative AI, this is achieved through mechanisms that make it easier to:

The shift from DRM to AI-RM represents not just a technological evolution, but a strategic necessity for businesses looking to harness generative AI, while maintaining robust compliance and risk management frameworks. AI-RM offers practical solutions through automated compliance checks, rights clearance, and certified audit trails. This presents significant opportunities for service providers, technology vendors, and consultants to develop integrated solutions that address the growing market demand for rightful and ethical AI.

The Limitations of Traditional DRM

With the advent of the Internet and the rise of digital content, Digital Rights Management (DRM) has been deployed as the primary tool for enforcing copyright protection, particularly in the context of preventing online piracy. However, DRM systems — designed to control the distribution and access to digital works — have been strongly criticized for their limitations, particularly as they intersect with user demands for access, fair use, and evolving legal frameworks.

Traditional DRM solutions are geared toward restricting how digital content can be copied, shared, and consumed. For example, in the music industry, companies like Apple employed DRM systems to restrict how songs purchased from their platforms can be transferred or played, often limiting playback to a specific set of authorized devices. In many cases, this restriction meant that users cannot enjoy their legally purchased content on the devices or software of their choosing, causing a significant gap between the consumer’s rights and the functionality of the product. As a result, these DRM-enabled products were often described as “defective by design,” since the DRM technologies not only imposed limitations on content use but also compromised the overall usability and quality of the product itself.

This approach has led to several inherent drawbacks. Firstly, DRM frequently undermines the balance between copyright enforcement and user freedoms, hindering legitimate consumer activities, such as format shifting or backup creation, that are usually allowed under copyright law’s “fair use” provisions. For instance, if a consumer wishes to use music for educational purposes or research, DRM restrictions may impede these uses even when they fall clearly within the scope of fair use, thus stifling cultural and intellectual exchange. DRM also shifted the norms around reselling. Before DRM, if people brought physical vinyls, CDs or books, for example, and grew tired of them, they could resell them. DRM reduced the scope of user rights to the resale of digital content impossible — going against established norms in the industry.

The inflexibility of traditional DRM also becomes apparent in cases of content preservation, as DRM can render it difficult or impossible to access or transfer digital media over time, further compounding issues of digital obsolescence and access to culture. Since DRM systems constrain digital content into a particular format that can only be read or accessed by authorised devices, it becomes increasingly difficult to preserve this content given the obsolescence of these devices.

More recently, the rise of generative AI has introduced a new set of challenges with regard to authorship, ownership, and fair use that traditional DRM systems are ill-equipped to address. Indeed, given the legal uncertainty around generative AI, it remains unclear whether an AI-generated work can be protected by copyright or whether it could be infringing if it is too similar to pre-existing copyrighted works upon which the AI was trained.

Hence, if DRM was originally designed as a tool for enforcing traditional copyright protections, the advent of AI-generated content demands a more nuanced approach that extends beyond simply protecting against piracy, but also provides safeguards against potential liability for copyright infringement. This is where AI Rights Management comes into play.

The Emergence of AI Rights Management

As Gen AI continues to redefine creative industries, the limitations of traditional DRM systems become ever more apparent. AI Rights Management emerges as a novel approach to address the challenges posed by AI-generated content.

Unlike traditional DRM, which primarily focuses on preventing the unauthorized reproduction and distribution of copyrighted works, AI-RM is not merely concerned with the protection of copyright, but also with the authenticity, provenance, legal and ethical dimensions of AI-generated works, ensuring that they respect existing rights and do not inadvertently infringe on copyrighted materials. By ensuring transparency, accountability, and compliance with intellectual property rights (as well as data protection and personality rights), AI-RM represents a defensive mechanism that reduces the likelihood of infringement. The goal of AI-RM is not to prevent or to restrict the use of AI models or AI-generated content — as traditional DRM do — but rather to provide a series of guarantees and certifications for people and companies willing to engage with generative AI in the course of their business that they can do so in a rightful manner. Indeed, by increasing the transparency and verifiability of the generative AI process, and by introducing attestations and certifications by model trainers or fine-tuners (e.g. developers of Low Rank Adaptations or LoRAs) concerning the process to build these models, it becomes possible to shift the locus of liability away from the end-users and towards the various players in the generative AI value chain that are responsible for making these commitments.

Traditional DRM systems designed to prevent unauthorized copying and sharing of content are effective at limiting the piracy of digital works, but do not address the more nuanced challenges arising from AI’s creative capacities. For instance, in the realm of AI-generated works, determining authorship, ownership, and the attribution of rights is extremely complex. Since the creative process is driven by machine learning algorithms that process vast amounts of data, it is unclear whether there is an ownership claim over the generated output, and, if so, who could make that claim — Is it the user who prompted the AI? the developers of the underlying model? the creators of the training datasets? or perhaps a combination of these? Moreover, Gen AI models can sometimes inadvertently generate content that is too similar to the copyrighted works it was trained on, raising concerns about copyright infringement and plagiarism — unless the relevant rights have been cleared and proper citations are provided.

To address these issues, generative AI model developers can provide certifications or attestations as regards the rightfulness use of AI models, guaranteeing that they have only been trained on public domain content (e.g. Spawning’s Public Domain) or on content that has been specifically licensed for this use case (e.g. Bria). This is particularly useful in the case of LoRAs, trained on smaller data sets, for which it is easier to obtain a clearance of rights from the relevant right-holders. Applying a certified LoRa on top of a foundational model — even if the model has been trained on pre-existing copyrighted materials (i.e. the ‘original sin’) — can significantly reduce, or even eliminate, the risk that the AI-generated output will be substantially similar to any of the works from the original training data set (c.f. Alias.studio).

In terms of provenance, AI-RM systems can provide verifiable records of how AI-generated works are created through mechanisms such as digital fingerprints and digital certificates. By tracking information such as the generative AI model and the associated parameters used to generate the work, along with the dataset used to train the AI model, AI-RM makes it possible for third-parties to verify the source and authenticity of any given piece of AI-generated content.

In addition to its tracking the origin or source of an AI-generated work, AI-RM can also be used to provide an auditable trail that keeps track of the chain of custody that the AI-generated work has gone through during its lifetime. This can be achieved, inter alia, with the use of blockchain technology, and in particular the use of non-fungible tokens (NFTs) to track the creation and transfer of AI-generated works along with their associated rights (see this post for more details).

Of course, while the theoretical framework for AI-RM provenance tracking is compelling, implementing it at scale faces significant technical hurdles — particularly around creating tamper-proof attribution chains across different AI models and platforms, standardizing provenance metadata formats, and ensuring interoperability between various tracking systems. Emergent blockchain-based solutions still need to overcome substantial coordination challenges between AI providers, content platforms, and rights holders before achieving widespread adoption.

2. Data privacy and personality rights

AI-generated content raises other ethical and legal questions with regard to data privacy and personality rights, which are often beyond the scope of traditional DRM systems merely concerned with protecting copyrighted content against piracy. With AI, there is a need to ensure that AI-generated works respect existing rights, especially in light of the fact that training datasets may include not only copyrighted material, but also personal data or confidential information.

An AI-RM system can tackle this problem by including links to the datasets used in training AI models, along with attestations by the model developers that these datasets do not contain proprietary or copyrighted works — unless permission has been obtained. Similarly, if a model is trained on a dataset containing personal information, the AI-RM system could provide detailed attestations verifying that the training data was properly anonymized according to industry standards, or that consent was given by all relevant parties whose data has been collected in the dataset. This is important as AI models are often trained on large datasets scraped from publicly available sources, which may include not only copyrighted content but also personal or confidential data. As such, AI-RM provides the necessary framework for balancing innovation with the responsible stewardship of intellectual property, data privacy, and personality rights.

3. Ensuring Compliance with Copyright Laws

AI-RM also plays a role in ensuring that AI-generated content complies with copyright and neighboring rights. Today, the legal status of AI-generated content remains unclear, as the conditions for it to qualify as an original work of authorship (and be eligible for copyright protection) are yet to be determined. Besides, AI-generated works could potentially infringe upon existing copyrights if they were regarded as a derivative of the copyrighted material on which the AI model was trained. Hence, while one cannot predict how copyright law will evolve in the future, existing regulations are already creating difficulties for companies or end-users to protect themselves from potential liability for copyright infringement.

Technological solutions have been put in place to address these challenges and reduce the liability risk for users of generative AI. For example, Spawning’s Do Not Train Registry allows right holders to explicitly opt-out from their works being used for training AI models, whereas the Have I Been Trained? tool makes it possible to search if a copyrighted work was incorporated into the training dataset of a specific AI model. Moreover, while cross-referencing AI outputs against known copyrighted works might be challenging, AI-RM can facilitate the licensing of specific datasets, ensuring that creators and developers know the legal boundaries in which they must operate (see this post for more details).

As such, AI-RM can help creators navigate the evolving (and still uncertain) scope of copyright law, especially as it relates to AI and machine learning. While current laws are often ambiguous when it comes to AI-generated works, AI-RM systems can assist in clarifying these ambiguities by providing a transparent record of how a work was generated and its relationship to existing content, with both technical and legal means.

AI-RM for ethical and responsible AI

With the rise of generative AI, novel challenges are emerging related to the risk of bias, the spread of misinformation, and the malicious use of generative AI for harmful purposes, such as creating deepfakes or propagating hate speech.

AI-RM can play a role in addressing these challenges by embedding specific values and ethical principles within the AI rights management framework. This can be done, for instance, through specific licensing schemes associated with a particular label or trademark, designed to govern how AI-generated works are used, shared, and distributed. By licensing different pieces of intellectual property — including datasets, models weights, training or inference software, etc. — under a license that explicitly requires every use of the IP to be associated with the label, it becomes possible to create a flourishing ecosystem around that label, which acts as a guarantee of ethical and responsible practices (what we have previously defined as the “Collaboration Monster”), as the license associated with the label can set clear boundaries around the types of content that can be generated by Gen AI models, ensure fair use of training data, and impose restrictions on the creation of AI-generated content and its conditions for dissemination.

AI-RM systems can integrate ethical guidelines directly into their licensing structures to promote responsible practices. For example, licenses such as the Copyfair license (or similar models) could explicitly outline the ethical boundaries and responsibilities associated with AI-generated content. The Copyfair license, which is an alternative to traditional Open Source or Open-RAIL licensing models, encourages the licensing of datasets or model weights in ways that prioritize fairness over openness, allowing for the introduction of specific restrictions that can be imposed on the use of Gen AI models, for ethical or commercial reasons. By incorporating this kind of licensing framework into AI-RM systems, creators and developers can be guided toward using AI technologies in ways that align with broader societal and ethical considerations, such as promoting openness, inclusivity, diversity, and fairness in AI-generated content.

For example, while mostGen AI models implement guardrails to ensure that the model does not generate harmful or offensive content, it is always possible to bypass these guardrails, e.g. by jailbreaking the model so that it generates undesirable content. To address that issue, an AI-RM system could include provisions within its licensing agreements to prevent the use of a Gen AI model to produce content that goes against public order or morality (akin to the restrictions introduced in the Responsible AI Licenses). Similarly, the license could prohibit using the model in specific contexts (e.g. military use), or limit its usage for sensitive subjects like politics, race, gender, or religion. This proactive approach would create a sense of responsibility among AI developers and creators, reinforcing the idea that AI is not just a tool for creative expression but also a technology that must be used with consideration for its potential impact on society.

2. Mitigating risks associated with bias and misinformation

Gen AI is particularly vulnerable to bias and the inadvertent amplification of misinformation, especially when models are trained on large datasets scraped from the internet. Without careful oversight, AI-generated content can thus reinforce societal biases or perpetuate false narratives, as AI models may mirror the prejudices present in their training data.

To address these challenges, the licensing frameworks within AI-RM systems could require creators to disclose the sources of their training data, ensuring transparency and accountability on the way the model has been trained. In addition, AI-RM can mitigate the risk of misinformation by providing means for model developers or fine-tuners to certify that, after having been analyzed through a bias detection tool, AI models do not display signs of bias or discrimination.

Yet, all models are likely to have an irreducible rate at which they hallucinate even if the underlying training data is “pristine”. AI-RM could introduce additional protections, by requiring models to mention the source of the information they are providing (e.g. Retriaval-Augmentation Generation for LLMs), or relying on third parties certifications to demonstrate that the AI output aligns with fact-checking standards, as a way to reduce the spread of misinformation.

3. Guarding against malicious use of AI

AI technologies, particularly generative models, have also been increasingly exploited for malicious purposes, such as creating deepfakes, fake news, or harmful propaganda. The rise of AI-generated disinformation is a serious concern, as it can erode public trust, manipulate opinions, and cause harm to individuals and communities.

The goal here is not to detect content generated by AI, but rather to identify whether such synthetic content is malicious or potentially harmful. While it is difficult to rely solely on technological means to detect these types of content, AI-RM can introduce contractual safeguards (e.g. licensing agreements) that explicitly prohibit the use of an AI model to generate certain types of harmful content, such as deepfakes or hate speech (e.g. via the Copyfair license).

Moreover, an AI-RM system could also include traceability features ensuring that the origins of AI-generated content are both verifiable and traceable. Watermarking technologies have already been developed, both in the context of LLMs and diffusion models. Yet, these approaches are likely to have arms race dynamics, because as models get better at generating more “realistic” content, it will necessarily be harder to differentiate them from content produced by a human. Moreover, it may also soon be possible for more sophisticated models to imitate the idiosyncratic patterns of less sophisticated models, in order to pretend that a particular piece of content was generated by another model.

An alternative approach consists in leveraging external attestations systems to record the source and provenance of content generated by AI. This can be done, for instance by recording the hash of the content on a blockchain and signing it with the private key of the actor generating it, as a guarantee of provenance and authenticity (see this post for more details). Again, the primary objective is not to determine whether or not a particular piece of content is synthetic, but rather to provably attribute such content to a specific generative AI model, or — even better — to a specific source. This is especially useful for actors eager to provide guarantees that their content, even if it is AI generated, has been endorsed by a particular entity and is therefore less likely to qualify as disinformation.

Finally, the certifications issued by AI-RM could be leveraged by third-party mechanisms for content moderation and ethics compliance monitoring, helping to enforce ethical standards at scale. While it is not possible to prevent the creation of deep-fakes or other malicious content, certifications can help us distinguish between “respectable” models which satisfy ethical content guidelines, and “rogue” models which do not provide any guarantee on their output. Hence, if a particular model or company is found to be creating low-quality or harmful content repeatedly despite its certifications, it could lose its credibility and adoption within specific platforms or services. Besides, through partnerships with fact-checking organizations or by embedding AI-powered content review systems, AI-RM could identify and flag harmful content before it spreads widely, offering an essential counterbalance to the various ways in which AI can be weaponized for unethical or malicious purposes.

Conclusion: AI-RM as a new business opportunity

Unlike traditional DRM, which often acts as a restrictive barrier that limits the consumer’s ability to use purchased content, AI-RM operates as a defensive framework that proactively ensures compliance with intellectual property laws and ethical standards. Rather than merely preventing unauthorized copying or distribution, AI-RM provides clarity around the ownership and use rights of AI-generated works, fostering a culture of accountability and transparency that serves the interests of both creators and consumers. As such, the emergence of AI Rights Management (AI-RM) marks a critical evolution in digital rights management, reflecting the need for a more sophisticated and flexible system that can handle the complexities of AI-driven content creation.

Indeed, as the need for legal certainty increases, AI-RM will become an essential infrastructure to be integrated in the workflow of a variety of stakeholders. For creators and enterprises, it can take the form of intuitive dashboards where users can monitor usage, manage permissions, and track content lineage across projects. Developers building AI applications may access API-based certification services that automatically validate training data compliance and generate attribution chains. For business users, AI-RM can integrate directly into existing content management systems, using smart contracts to automate rights clearance and providing real-time alerts about potential compliance issues.

This infrastructure enables AI to serve as a powerful tool for creativity by providing clear frameworks for legitimate use of training data and generated content. When creators know exactly what they can and cannot do with AI tools, and when attribution and rights are automatically tracked, they can focus on innovation rather than compliance concerns. The automated tracking and verification of rights creates a “safe space” for experimentation, where creators can freely combine AI-generated content with their own work, remix existing materials, and build upon others’ creations without fear of inadvertent infringement. With clear documentation of rights and contributions, creators can more easily collaborate and build upon each other’s work, fostering a more dynamic creative ecosystem. Real-time verification of rights and automated attribution chains eliminate the need for creators to manually track and verify permissions, reducing cognitive overhead during the creative process.

Furthermore, this clarity creates significant market opportunities as organizations seek to implement these systems. The market for AI-RM solutions encompass not just technical infrastructure (e.g. blockchain-based certification systems,, and API services for real-time rights verification), but also consulting services for implementation, compliance monitoring tools and risk assessment services, as well as a variety of integration services such as automated attribution and licensing management. As enterprises increasingly rely on AI for content creation, the demand for rights management solutions will continue to grow, creating opportunities for technology providers, consultants, and service operators to help organizations navigate this new landscape effectively while maintaining legal compliance and ethical standards.