Generative AI has revolutionised creativity, technology, and business. From producing art and music to drafting text and code, these systems rely on enormous datasets derived from existing human-created works. This dependence raises a crucial legal question: under Indian copyright law, is it permissible to use protected material for AI training? The issue is far from theoretical—it directly affects whether developers can train models on copyrighted works, whether such use qualifies as fair dealing, and what precautions companies should adopt before deploying AI tools.
India’s legal framework on AI copyright remains unsettled. Unlike some countries that have created specific exemptions for text and data mining (TDM), India continues to operate within the conventional boundaries of the Copyright Act, 1957. This creates a legal grey area for AI training, leaving innovators, creators, and regulators searching for clarity.
Fair Dealing and AI Training under Indian Copyright Law
The doctrine of fair dealing is central to this debate. Indian copyright law provides a closed list of exceptions—such as private use, criticism, reporting, judicial proceedings, education, and research—where copyrighted works can be used without permission.
When applied to AI, the issue is whether feeding copyrighted works into training datasets qualifies as one of these exceptions. Developers often argue that training is a non-expressive use, meaning the system does not replicate works directly but extracts patterns. However, Indian courts have historically interpreted fair dealing narrowly. As a result, there is no certainty that AI training would fall within these exceptions.
For companies, this creates legal risk. Unless Parliament introduces a clear exception for AI-related activities, training on copyrighted material could be treated as infringement. This makes the question of AI copyright less about policy and more about managing compliance under an ambiguous law.
Text and Data Mining in India
Text and data mining (TDM)—the process of analysing large datasets to extract insights—is essential for developing AI. Yet, in India, its legality depends on the nature of the works being mined and the purpose of use.
The European Union has recognised TDM by introducing explicit statutory exceptions, but India has not done so. While Indian law allows use of copyrighted works for research or private study, this has traditionally been interpreted as human research, not large-scale automated mining by algorithms.
This leaves developers with limited safe options:
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Use public domain or open-access content (e.g., Creative Commons works).
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Obtain licences from rights holders.
Without these steps, mining copyrighted works for AI training could trigger infringement claims. In the absence of statutory guidance, contracts and compliance mechanisms become critical for businesses and research institutions working with generative AI in India.
Training Data Copyright in India
Training data is the backbone of generative AI systems. Whether it includes novels, music, journal articles, or digital art, these works influence how AI generates outputs. However, much of this material is protected by copyright, raising the question: does using such data amount to reproduction and infringement under Indian law?
The Copyright Act, 1957 gives authors exclusive rights to reproduce, adapt, and distribute their works. Since AI training involves making copies—albeit temporary—for analysis, this process may fall under reproduction rights. Indian courts have not yet ruled on whether these technical copies count as infringement, but the absence of an exemption leaves developers vulnerable.
Complications also arise when datasets mix public domain works with copyrighted materials. Public domain content is safe, but copyrighted material generally requires licensing unless it falls within a fair dealing exception. To avoid disputes, businesses are encouraged to conduct copyright audits of their training datasets before development or deployment.
Contractual Safeguards in AI Development
Given the uncertainties of statutory law, contractual arrangements are becoming the most effective safeguard. For organisations developing or deploying AI models in India, carefully drafted agreements can help mitigate risk and allocate responsibilities.
Key safeguards include:
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Licensing agreements with data providers to clearly define rights to use datasets. These contracts should specify whether use is limited to research, commercial development, or resale.
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Warranties and indemnities where service providers confirm that training data is lawfully sourced, shifting risk in case of third-party infringement claims.
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User contracts for businesses integrating AI tools, clarifying ownership of AI-generated outputs and allocating liability if generated content infringes existing copyrights.
By embedding these safeguards, companies can better protect themselves while operating in a legally uncertain environment.
Conclusion
India is at a pivotal point in defining the relationship between generative AI and copyright law. The current law does not expressly accommodate text and data mining or AI training, but the rapid evolution of AI makes reform urgent. Lawmakers may need to consider limited statutory exceptions for non-commercial TDM and research while requiring licensing for commercial applications.
Until then, businesses must combine careful dataset selection, reliance on open-access works, and strong contractual safeguards to manage copyright risks. A balanced approach—patents where possible, trade secrets where appropriate, and robust licensing for data use—can help AI innovators in India build sustainable and legally compliant practices.
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