Two countries on opposite sides of the world are tackling the same urgent problem: how to regulate artificial intelligence without copying Western models wholesale. Iraq is drafting its first comprehensive AI governance framework for commercial companies, while India's Supreme Court is establishing rules for AI use in courts. Their parallel efforts reveal a critical gap in global AI policy: most nations lack tailored regulatory approaches that fit their own legal traditions and institutional realities.

Why Can't Countries Just Adopt Existing AI Rules?

The European Union's AI Act and similar Western frameworks have set a global standard, but they don't translate directly to every legal system. Iraq's experience illustrates this challenge. A new academic study examining Iraq's legal landscape found that existing Iraqi laws can address some AI-related issues, but the provisions remain fragmented, largely interpretative, and reactive. The country lacks a clear legal definition of artificial intelligence, risk-based classification systems, and comprehensive data protection legislation. Simply copying the EU's approach would ignore Iraq's unique institutional context and legal traditions.

India faces a parallel challenge in the judicial system. The Supreme Court of India released draft regulations for AI use in courts in 2026, recognizing that courts need AI governance tailored to judicial independence, constitutional safeguards, and India's emerging data protection architecture. The Cyril Shroff Centre for AI, Law and Regulation at O.P. Jindal Global University submitted detailed comments on these draft regulations, emphasizing that the framework must protect core procedural and constitutional protections while enabling responsible innovation.

What Are the Key Gaps These Countries Are Trying to Fill?

Iraq's analysis identified several critical legislative gaps that many developing nations likely share:

• Liability Allocation: No clear rules determining who is responsible when an AI system causes harm, whether the company deploying it, the software supplier, or both
• Data Protection: Absence of comprehensive legislation governing how AI systems collect, store, and use personal information from employees and consumers
• Algorithmic Discrimination: No explicit protections against AI systems that discriminate based on protected characteristics like gender, ethnicity, or disability
• Employee Rights: Unclear legal status of workers' rights when AI systems monitor, evaluate, or replace human labor
• Consumer Protection: Limited safeguards for individuals subject to AI-driven decisions in commercial transactions
• Right to Explanation: No legal requirement for companies to explain how AI systems make decisions affecting individuals

India's judicial context adds another layer: courts need AI governance that preserves human oversight, maintains transparency in legal proceedings, and protects judicial independence. The Supreme Court's draft regulations attempt to address these concerns while enabling courts to benefit from AI tools for case management and legal research.

How Are Iraq and India Building Context-Sensitive Frameworks?

Rather than importing foreign models wholesale, both countries are developing hybrid approaches rooted in their own legal systems. Iraq's study proposes the IR-CAGM (Iraqi Commercial AI Governance Model), a phased framework that connects legislation, sectoral oversight, corporate responsibility, impact assessment, life-cycle governance, and individual rights. This model acknowledges that different industries and use cases require different levels of oversight, and that accountability must be distributed across multiple stakeholders.

India's approach similarly emphasizes context sensitivity. The Cyril Shroff Centre's submission to the Supreme Court focused on six key areas: the legal basis of the regulatory framework, meaningful human oversight over AI-assisted processes, judicial data governance, public transparency and accountability mechanisms, protection of core procedural and constitutional safeguards, and sequencing implementation alongside India's emerging data protection architecture. This reflects recognition that judicial AI governance cannot be separated from broader national data protection efforts.

What Does This Mean for Global AI Governance?

Iraq and India's efforts suggest that effective AI governance requires more than transplanting rules from the EU or United States. Both countries are demonstrating that regulatory frameworks must account for local legal traditions, institutional capacity, and specific sectoral needs. Iraq's model emphasizes that the goal is not to impede responsible AI adoption, but to support development of a governance framework that is constitutionally sound, operationally coherent, accountable, and enforceable.

The timing matters too. India's Supreme Court is moving quickly to establish judicial AI rules before widespread deployment creates entrenched practices that are harder to regulate. Iraq's legislative analysis suggests that countries without existing AI-specific laws face a window of opportunity to build comprehensive frameworks before fragmented regulations accumulate.

These parallel efforts also highlight a broader pattern: nations outside the Western regulatory sphere are increasingly building their own AI governance capacity rather than waiting for international consensus. This decentralization of AI governance could create a more diverse regulatory landscape, with different countries experimenting with different approaches to balancing innovation, accountability, and rights protection. Whether this fragmentation strengthens or weakens global AI governance remains an open question.