Artificial intelligence is changing the world at breakneck speed. New AI systems are coming out nearly every week, and each is more capable than the previous one. But while these technologies change our lives, societies are rushing to put proper guardrails in place. This article will attempt to guide you through the seven most immediate regulatory challenges confronting AI technologies today, particularly from an Indian standpoint.
- Privacy and data protection
AI programs live on data—gigantic quantities of it. This brings inherent conflicts with privacy protections. When you interact with a chatbot or an image generator, where does your data end up? Who sees it? For how long?
India’s Digital Personal Data Protection Act would try to answer these questions by mandating express consent for the collection of data. But executing this effectively means solving hard questions: What is meaningful consent? How can consumers really know the sophisticated ways that AI may process their data? And what if AI systems infer things about individuals who never agreed to be part of the dataset in the first place?
- Bias and discrimination
AI algorithms are taught using past information—which tends to have biases embedded within it. Unchecked, such systems can reproduce and even extend discrimination.
Consider recruitment AI, which has become increasingly popular among Indian businesses seeking to automate hiring. When trained on historical hiring choices that benefited particular groups, such systems tend to replicate the same biases.
Regulators confront a difficult balancing act: how to foster equity without inhibiting innovation? Must firms pretest AI systems for bias prior to deployment? What criteria would apply? These questions are still largely unresolved in India’s existing regulatory system.
- Safety and security threats
As capabilities in AI increase, so do potential threats. Large language models can create believable disinformation. Image generators can produce deepfakes. AI systems that run critical infrastructure may be susceptible to attacks.
Creating proper safety standards is challenging since most risks are still theoretical. How do you control for harms that have not yet occurred? Do high-risk AI systems need to be certified prior to deployment? Who gets to determine what is “high-risk”?
- Transparency and explainability
Most contemporary AI systems are “black boxes”—even their developers cannot explain some decisions. This raises significant regulatory issues, particularly when such systems make high-stakes decisions about individuals’ lives.
Consider the case of credit scoring. If a loan application is denied on the basis of an AI algorithm’s judgment, shouldn’t the applicant be entitled to know why? The Reserve Bank of India has started addressing this by issuing guidelines mandating financial institutions to offer explanations for auto-decisions, but enforcement is patchy.
The technical hurdle is large: making neural networks complex but fully explainable can be at the expense of their performance. Regulators have to decide on the degree of transparency needed for various uses, weighing the value of innovation against the need for accountability.
- IP and copyright
AI models that are trained on copyrighted materials pose questions regarding IP rights. When an AI produces a song that sounds like mainstream Indian artists following training on their songs, who owns the end product? Shouldn’t the original artists be paid?
This isn’t hypothetical—it’s already occurring. In 2025, leading music houses alleged that AI-generated songs used songs whose copyright was owned by these music houses as training material. This has led to a court battle, underlining the shortcomings of existing copyright laws for AI-created content.
Most copyright laws never envisioned such situations. Regulators have to tread carefully between safeguarding creators’ rights and facilitating useful innovation.
- Liability and accountability
When AI systems do harm, who is responsible? The developer? The deployer? The end user? Existing liability frameworks aren’t framed for autonomous systems.
A hypothetical scenario such as a self-driving car crash would bring this gap in regulation into sharp focus. Assigning liability would be incredibly complicated. Is it the fault of the software programmer, the vehicle manufacturer, or the individual who was not driving the car? With AI becoming more autonomous and ubiquitous, it is imperative to have well-defined liability structures in place.
- Cross-border regulation
AI does not care about national boundaries. A model developed in America can be used in India, trained on data obtained from the world, and affect users globally. This poses huge challenges for regulators at the national level.
India’s approach has been to practice “digital sovereignty,” that is, data localisation obligations. But tensions exist between promoting indigenous AI innovation and participating in international AI governance. How do Indian regulators balance protecting citizens while maintaining domestic companies’ competitiveness abroad?
The question becomes even more complicated when put into cultural perspective. AI moderation platforms intended for Western markets have a tendency to misunderstand Indian cultural sensitivities, censoring content in the wrong way. Should India demand culturally-specific AI systems for its market?
The Road Ahead
As we work our way through these regulatory hurdles, India has the chance to create a harmonious strategy that safeguards people while supporting innovation. Recent efforts at regulating financial technology have proved that even old guard institutions such as NBFCs can evolve to keep up with technological advancement, presenting some potential directions to follow. The proliferation of AI in businesses such as online marketplace illustrates how well-designed guardrails can allow for productive growth while reducing risks. The secret is carefully thought-through regulation that develops in parallel with the technology itself.
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English (US) · 