AI Governance Race Intensifies: India's Public Infrastructure Model vs. EU's Deepfake Labels

The global scramble to regulate artificial intelligence has entered a critical implementation phase, with two distinct governance philosophies taking shape. On one side, India is championing a public infrastructure model, while the European Union is enforcing detailed content regulations. For cybersecurity leaders, this regulatory divergence presents both challenges and a roadmap for building more secure AI ecosystems.

India's Digital Public Infrastructure Vision for AI

The Indian government, through its Ministry of Electronics and Information Technology (MeitY), has released a strategic white paper arguing that artificial intelligence should be treated as Digital Public Infrastructure (DPI) rather than proprietary technology. This approach builds on India's successful deployment of DPIs like Aadhaar (digital identity) and UPI (payment system), which are open, interoperable, and secure by design.

The core cybersecurity argument is that treating AI as public infrastructure mitigates systemic risk. Proprietary, closed AI systems create single points of failure and vendor lock-in, making national digital ecosystems vulnerable to exploitation, backdoors, and unsustainable dependencies. The DPI model advocates for open standards, transparent algorithms where possible, and security audits as a public good. MeitY has also released an accessibility guide aimed at democratizing AI tool access, which from a security perspective, includes guidelines for building inclusive and secure interfaces that don't create new attack vectors for disadvantaged users.

The EU's Content-Centric Approach: Mandatory Deepfake Labeling

In contrast, the European Union is moving to implement the world's first comprehensive AI Act, with a sharp focus on regulating output. A key provision now coming into force is the mandatory labeling of AI-generated content and deepfakes. This is a direct regulatory response to the cybersecurity and disinformation threats posed by sophisticated synthetic media.

The regulation requires that any AI-generated image, video, or audio content that could be mistaken for real human output must carry a clear, machine-readable label. This creates a new compliance layer for platforms and content creators and a new defensive tool for security teams. The labels are intended to act as a "nutrition facts" panel for digital content, allowing detection systems and users to assess provenance. For cybersecurity operations, this means integrating new verification protocols and potentially developing tools to detect non-compliant or tampered-with labels.

India's Push for Global Consensus and the 2026 AI Summit

Recognizing the fragmentation risk, India is actively seeking to bridge these regulatory approaches. MeitY Secretary S. Krishnan has announced that India will push for a global consensus on AI governance norms at the upcoming AI Summit in 2026. The goal is to establish foundational principles that harmonize security requirements across jurisdictions, preventing a regulatory patchwork that could be exploited by threat actors. India's position leverages its DPI experience to argue for global standards that ensure security, equity, and openness.

Implications for the Cybersecurity Profession

These parallel developments signal a profound shift for cybersecurity teams:

1. From Tool Management to Governance Assurance: The role is expanding from securing AI tools to ensuring entire AI systems comply with regional regulations. Teams will need to map data flows, model development processes, and output channels against frameworks like the EU AI Act or India's DPI principles.
2. New Technical Requirements: The EU's labeling mandate will require technical stacks to generate, embed, and verify metadata tags at scale. Security architects must design systems that can't be easily stripped of these labels, a new front in the cat-and-mouse game with malicious actors.
3. Supply Chain and Vendor Security: India's DPI model emphasizes avoiding proprietary lock-in. This will force rigorous third-party risk assessments for AI vendors, with a focus on code transparency, data sovereignty, and the right to audit—factors previously often overlooked in favor of capability.
4. Deepfake Detection as a Core Competency: Regardless of the regulatory path, the threat of synthetic media is now a board-level concern. Cybersecurity teams will need to invest in or develop capabilities for detecting unlabeled deepfakes used in business email compromise, influence operations, and fraud.

The Road Ahead: A Bifurcated Regulatory Landscape

The emerging landscape suggests a bifurcation: a content-regulation path led by the EU focusing on transparency of output, and an infrastructure-regulation path championed by India focusing on the security and openness of the underlying systems. For multinational corporations, this means implementing flexible governance frameworks that can adapt to both. The ultimate test will be whether these regulations can be enforced technically and whether they genuinely reduce the attack surface of AI, or simply add a new layer of complexity for defenders. The outcomes of India's 2026 summit push and the real-world efficacy of the EU's labeling will be critical indicators for the future of secure AI.