# Can generative AI be regulated?
_Published 2026-07-16T06:32:39.778Z · Updated 2026-07-16T06:34:00.908Z · By Aniruddh Atrey_
Canonical: https://www.courtnetra.com/blog/can-generative-ai-be-regulated
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> Explore the legal challenges of generative AI, from copyright and deepfakes to regulation, transparency, and India's evolving AI governance framework.
Generative Artificial Intelligence has rapidly transformed from a niche technological innovation into a tool that is reshaping industries, education, healthcare, finance, and even the legal profession. Systems capable of generating human-like text, images, code, and videos have unlocked unprecedented opportunities for innovation, but they have also exposed significant gaps in existing legal frameworks. As companies such as OpenAI and Google continue to push the boundaries of AI capabilities, regulators across the world are confronted with a pressing question: how can the law encourage innovation without compromising intellectual property, privacy, transparency, and public trust?

One of the most debated legal issues surrounding generative AI is the use of copyrighted material for training large language models. These systems are developed using enormous datasets that often include publicly available books, articles, websites, and creative works. While AI developers argue that such use is transformative and essential for technological advancement, creators contend that their work is being used without consent or compensation. This tension has sparked several copyright disputes and revived discussions around the scope of fair use and fair dealing doctrines. Although judicial approaches continue to evolve, these disputes demonstrate that traditional copyright principles may require reinterpretation in the age of artificial intelligence.

Beyond intellectual property, generative AI has amplified concerns relating to misinformation, deepfakes, and digital impersonation. AI-generated content can closely mimic human expression, making it increasingly difficult to distinguish authentic information from fabricated material. This raises complex legal questions regarding accountability. If an AI-generated output causes reputational harm, spreads false information, or facilitates fraud, determining liability becomes challenging. Responsibility could potentially lie with the developer, the platform provider, the user, or a combination of these actors. Existing legal frameworks were largely designed around human conduct and therefore struggle to accommodate autonomous or semi-autonomous systems capable of generating content at scale.

Recognizing these challenges, regulators have begun shifting their focus from reactive enforcement to proactive governance. The European Union's AI Act reflects this approach by introducing transparency obligations, risk-based classifications, and compliance requirements for high-risk AI systems. Rather than regulating AI only after harm occurs, the framework seeks to embed accountability throughout the lifecycle of AI development and deployment. Although regulatory models differ across jurisdictions, the underlying objective remains consistent: ensuring that technological innovation develops alongside adequate safeguards for individuals and society.

The approaches adopted by OpenAI and Google illustrate how industry practices are gradually aligning with these emerging expectations. Both organizations have introduced safety mechanisms, content moderation policies, and transparency initiatives designed to reduce misuse of their AI systems. Google has explored content authentication technologies and watermarking, while OpenAI has implemented usage policies and continuous model evaluation to minimize harmful outputs. Although these measures are primarily voluntary, they demonstrate that responsible AI governance is becoming an essential component of product development rather than merely a regulatory obligation.

India currently stands at an important stage in its AI governance journey. While the Digital Personal Data Protection Act, 2023 strengthens the country's privacy framework, it does not comprehensively regulate generative AI or address issues such as copyright ownership, algorithmic accountability, or AI-generated misinformation. Consequently, existing laws, including the Information Technology Act, the Copyright Act, and intermediary liability provisions, continue to provide the primary legal framework for addressing disputes involving AI-generated content. As AI adoption accelerates across sectors, however, there is an increasing need for a more cohesive regulatory approach that balances innovation with legal certainty.

The experiences of OpenAI and Google suggest that effective AI regulation cannot rely solely on prohibitions or litigation. Instead, it requires a combination of legal safeguards, ethical design principles, technological transparency, and institutional accountability. Generative AI is likely to become deeply integrated into business operations, governance, and public services, making trust an essential prerequisite for its widespread adoption. As lawmakers continue to navigate this evolving landscape, the challenge will not be whether artificial intelligence should be regulated, but how regulation can remain sufficiently adaptive to keep pace with one of the fastest-moving technologies of the modern era.