EU Antitrust Probes and India's Copyright Proposal Reshape AI Legal Landscape

The legal foundations of artificial intelligence are undergoing simultaneous stress tests in two major regulatory theaters, creating what experts are calling "the perfect regulatory storm" for AI development. In Europe, antitrust authorities have trained their sights on Google's AI practices, while in India, policymakers are considering a copyright framework that could either unlock or destabilize AI training markets globally. For cybersecurity and compliance leaders, these developments signal that the rules governing AI's most critical input—data—are being rewritten in real time, with profound implications for risk management and corporate strategy.

The European Commission's formal antitrust investigation represents a significant escalation in regulatory scrutiny of Big Tech's AI ambitions. At the heart of the probe is whether Google has engaged in anti-competitive conduct by using copyrighted material to train its AI models, particularly for features like AI Overviews in search results. Regulators are examining whether this practice creates an unfair advantage that competitors cannot overcome, potentially locking them out of the market. The investigation focuses on whether Google's access to vast amounts of copyrighted content—through its search index, YouTube, Books, and other services—constitutes a barrier to entry that violates EU competition rules.

What makes this investigation particularly consequential is its intersection of copyright law with competition policy. Traditionally, these legal domains have operated separately, but the EU is now examining how control over training data might create market power that competition law should address. The Commission is reportedly investigating whether Google's practices could "restrict competition in the market for AI-powered online search services" by leveraging its existing dominance in search and digital advertising.

Parallel to these European developments, India is pursuing a dramatically different approach. A government-appointed committee has proposed creating a statutory licensing framework that would allow AI companies to use copyrighted works for training their models upon payment of a government-determined fee. This "blanket license" system would represent one of the world's most permissive regimes for AI training data, potentially making India a hub for AI development while testing the limits of international copyright norms.

The Indian proposal, currently open for public consultation, seeks to balance the rights of creators with what the committee describes as "the larger public interest in fostering AI innovation." Under the proposed framework, AI developers would gain legal certainty for using copyrighted materials, while rights holders would receive compensation through a collective licensing mechanism. This approach contrasts sharply with the EU's more restrictive stance and ongoing litigation in the United States over fair use exceptions for AI training.

For cybersecurity professionals, these regulatory shifts create multiple layers of risk that extend beyond traditional compliance concerns. First, the provenance and legality of training data are becoming critical security and compliance issues. Organizations must now implement robust data governance frameworks that can track the lineage of training data, document licensing arrangements, and demonstrate compliance across multiple jurisdictions with conflicting requirements.

Second, the antitrust dimension introduces new operational risks. Companies that achieve significant market share in AI services may face not just regulatory scrutiny but mandatory interoperability or data-sharing requirements. This could force organizations to redesign their AI architectures to accommodate potential data portability mandates, creating new attack surfaces and integration vulnerabilities.

Third, the Indian proposal, if implemented, could create a bifurcated global market for AI development. Companies might be tempted to base their training operations in jurisdictions with more permissive rules, creating complex data sovereignty and cross-border transfer challenges. Cybersecurity teams would need to manage data pipelines that span multiple legal regimes with different security requirements and oversight mechanisms.

Technical implications are equally significant. The regulatory pressure is likely to accelerate development of several key technologies:

1. Provenance Tracking Systems: More sophisticated cryptographic and blockchain-based systems for documenting data lineage from source to model weights.
2. Differential Privacy and Synthetic Data: Increased investment in techniques that allow model training without direct access to copyrighted or sensitive source material.
3. Compliance-Aware AI Architectures: Modular systems designed to accommodate different data handling rules based on jurisdiction and data type.
4. Automated Rights Management: AI systems capable of identifying copyrighted content, assessing permissible uses, and managing royalty payments.

From a strategic perspective, these developments suggest that control over high-quality training data may become the next major antitrust battleground in digital markets. Companies that can secure privileged access to data—whether through partnerships, acquisitions, or regulatory capture—may gain sustainable competitive advantages. Conversely, regulators appear increasingly willing to intervene to ensure competitive markets, even if that means limiting how companies can use data they have collected.

The cybersecurity industry itself faces both challenges and opportunities in this new landscape. Security vendors will need to develop solutions that help organizations manage AI compliance risks, including tools for data classification, rights management, and regulatory reporting. At the same time, security teams must prepare for more complex threat environments where adversaries might exploit regulatory differences between jurisdictions or target the new data pipelines created by these regulatory frameworks.

As these regulatory narratives unfold in Europe and India, they are creating what legal scholars describe as "regulatory crossfire"—conflicting pressures that make coherent global strategy difficult. Companies developing or deploying AI systems must now navigate not just technical challenges but a rapidly evolving legal landscape where the rules of engagement are being written simultaneously in multiple forums with different philosophies and objectives.

The ultimate impact may be a fundamental reshaping of how AI systems are built and deployed. The era of training models on whatever data is available may be giving way to a more constrained environment where data rights, competitive concerns, and national interests play determining roles. For cybersecurity leaders, this means expanding their purview beyond traditional security concerns to encompass the complex interplay of legal, competitive, and technical factors that will define AI risk in the coming decade.