Mexico’s Federal AI Law: A Comprehensive Framework for Responsible Innovation
Mexico is moving decisively toward comprehensive AI regulation through its proposed Federal Law Regulating Artificial Intelligence (Ley Federal para la Regulación de la Inteligencia Artificial). The bill introduces a risk-based compliance framework, establishes a National Commission for Artificial Intelligence (CONAIA), and mandates authorization, transparency, and accountability for high-risk AI systems. As of October 2025, the bill remains under discussion in Mexico’s Senate Commission on Science and Technology, with final approval expected in 2026. Once enacted, it will position Mexico among Latin America’s AI-governance leaders alongside Chile and Brazil and align national practices with global frameworks such as the EU AI Act and OECD AI Principles.
Understanding Mexico’s AI bill
The Federal Law Regulating Artificial Intelligence, introduced in 2023 by Senator Alejandra Lagunes, seeks to balance technological innovation with robust safeguards against harm. It reflects international best practices while adapting them to Mexico’s socio-economic priorities, particularly the ethical deployment of AI in finance, manufacturing, and public services. Mexico’s National AI Strategy 2.0, launched in 2025 by the Secretariat of Economy, complements the bill by emphasizing trustworthy AI, open data infrastructure, and sustainable innovation.
Key components of Mexico’s AI framework
1) Establishment of the National AI commission (CONAIA)
The law creates the Comisión Nacional de Inteligencia Artificial (CONAIA), a decentralized body under the Ministry of Economy, serving as the central authority to ensure that all AI systems developed or deployed in Mexico meet safety, fairness, and transparency standards. Its mandate covers four main pillars:
1. Oversight - Monitoring and auditing AI systems nationwide
CONAIA will act as Mexico’s primary supervisory authority for AI. It will register all AI systems operating within Mexico’s jurisdiction, monitor compliance with risk-management, transparency, and safety obligations, conduct audits and inspections of AI developers and deployers to verify documentation, testing, and human-oversight mechanisms and investigate incidents such as bias, discrimination, or harm caused by AI decisions, and recommend corrective actions. This continuous oversight ensures accountability and builds public trust in AI technologies.
2. Authorization - Approving high-risk AI before market entry
High-risk AI systems, such as those used in healthcare, law enforcement, employment, or credit scoring cannot be placed on the Mexican market without prior approval. CONAIA will review applications submitted by developers, including risk assessments, technical documentation, and testing results, grant or deny authorization based on compliance with safety, transparency, and ethical-use requirements, and reassess approvals periodically or after significant system changes (e.g., retraining or new data inputs). This pre-market control mechanism prevents unsafe or biased systems from reaching users.
3. Guidance - Issuing sector-specific codes and technical standards
CONAIA will also act as a policy and technical advisory body for AI governance. It will develop sector-specific guidelines, such as for healthcare AI, autonomous vehicles, financial algorithms, or education platforms, define technical standards for testing, explainability, and data quality, often in cooperation with standardization bodies and industry experts, publish best-practice frameworks to help businesses interpret the law and integrate compliance by design. This guidance function bridges the gap between legal requirements and practical implementation.
4. Coordination - Aligning policies across Mexican regulators
Because AI intersects with multiple domains, CONAIA will coordinate with existing authorities to ensure coherence across Mexico’s regulatory system. The following table discusses the primary roles of the various regulatory authorities. This inter-agency cooperation ensures that Mexico’s AI governance is integrated, consistent, and adaptable to technological and ethical challenges.
| Regulatory Authority | Full Name | Primary Role in AI Governance |
|---|---|---|
| INAI | Instituto Nacional de Transparencia, Acceso a la Información y Protección de Datos Personales | Ensures compliance with data-protection and privacy obligations under the Ley Federal de Protección de Datos Personales en Posesión de los Particulares (LFPDPPP). Oversees automated-decision systems involving personal data and enforces transparency rights. |
| COFECE | Comisión Federal de Competencia Económica | Prevents anti-competitive behavior, algorithmic collusion, or market manipulation resulting from AI-driven decision systems. Reviews mergers and market practices involving algorithmic pricing or recommendation engines. |
| PROFECO | Procuraduría Federal del Consumidor | Protects consumer rights in AI-enabled goods and digital services. Ensures fairness, transparency, and non-deceptive commercial practices in AI-based interactions and automated contract terms. |
| IFT | Instituto Federal de Telecomunicaciones | Regulates AI applications in communications, broadcasting, and digital infrastructure. Oversees network neutrality, algorithmic content curation, and cybersecurity standards for AI-driven telecom systems. |
Table 1. 0 Inter-Agency Coordination Framework
Together, these four functions form the foundation of Mexico’s AI oversight ecosystem, combining enforcement, guidance, and policy coordination.
2) Risk-based classification
The following three-tier model mirrors the EU AI Act and ensures proportional regulation.
| Risk Category | Examples | Obligations |
|---|---|---|
| Prohibited AI | Social scoring, subliminal manipulation, real-time biometric surveillance (except security exemptions) | Strict ban |
| High-Risk AI | Recruitment, education, credit scoring, healthcare, law enforcement | Authorization, documentation, human oversight |
| Limited/Low Risk AI | Chatbots, recommender systems | Transparency and user disclosure only |
This risk-based model ensures that regulatory attention scales with potential societal impact, avoiding unnecessary barriers for low-risk innovation.
3) Design and authorization requirements
Developers and deployers must submit detailed documentation to obtain CONAIA authorization for high-risk AI systems. Organizations seeking authorization to deploy high-risk AI systems under Mexico’s proposed Federal Law Regulating Artificial Intelligence must prepare and maintain comprehensive documentation demonstrating the system’s safety, fairness, and accountability. Key Requirements for High-Risk AI Authorization are:
1. Risk Assessments
Developers must conduct detailed analyses to identify foreseeable risks and potential harms throughout the AI system’s lifecycle - including technical failures, bias, misuse, or societal impacts. Each identified risk must be accompanied by mitigation and control measures showing how the organization prevents or minimizes harm to individuals and groups.
2. Technical Documentation
Applicants must provide complete technical documentation describing the system’s architecture, data sources, training methodology, model parameters, and performance metrics. This documentation should be sufficient for CONAIA or accredited auditors to evaluate the AI system’s functionality, traceability, and compliance with legal and ethical standards.
3. Testing Results
Before market entry, organizations must present evidence of safety, robustness, and reliability based on laboratory testing, validation datasets, or real-world pilots. Testing should confirm that the system performs consistently under expected operating conditions and that any limitations are clearly defined and disclosed.
4. Human Oversight Plan
Each high-risk system must include a documented human-oversight mechanism ensuring that critical decisions remain under meaningful human control. This plan should specify who is responsible for intervention, what triggers human review, and how oversight procedures are integrated into operational workflows.
5. Post-Market Monitoring
Once deployed, AI providers must establish ongoing monitoring and reporting systems to track performance, detect anomalies, and address new risks that emerge over time. Organizations must report serious incidents or system malfunctions to CONAIA and update their documentation accordingly.
These requirements create a lifecycle approach to compliance, from design and testing to deployment and post-market monitoring. Systems involving personal data must comply with the Federal Law on Protection of Personal Data (LFPDPPP) and INAI’s 2024 Guideline on Automated Decision-Making.
4) Prohibitions and safeguards
The bill prohibits AI systems that manipulate human behavior through subliminal influence, exploit vulnerabilities of individuals or groups, conduct generalized social scoring and enable indiscriminate biometric surveillance. Special authorizations may apply for national security or criminal-investigation purposes, under strict proportionality principles.
5) Transparency, reliability, and fairness
Organizations must clearly inform users when AI systems influence decisions affecting them. Disclosures must include system capabilities, limitations, and explainability mechanisms. Developers are responsible for maintaining consistent performance. They must conduct regular audits, implement incident-response procedures, and ensure ongoing model validation. Bias testing and dataset diversification are mandatory across the AI lifecycle. CONAIA will issue technical guidelines to detect and correct discriminatory outcomes.
Obligations for generative AI and large language models (LLMs)
Under Mexico’s proposed AI framework, Generative AI systems and Large Language Models (LLMs) are subject to enhanced transparency and accountability obligations due to their potential societal and informational impact.
1. Labeling and Disclosure
All AI-generated text, images, audio, or video content must be clearly labeled as synthetic. Providers must ensure that users can easily distinguish between human-created and AI-generated material in any digital or commercial context.
2. Content Moderation and Quality Controls
Developers and deployers must implement robust moderation mechanisms to detect and filter inaccurate, harmful, or manipulative content. Quality-control safeguards should address misinformation, deepfakes, and automated amplification risks.
3. Bias Monitoring and Fairness Audits
Providers are required to conduct ongoing evaluations of model outputs to identify and correct biased or discriminatory patterns. Fairness metrics and audit trails must be maintained as part of the system’s compliance documentation.
4. User Awareness and Transparency
Organizations must inform users about the capabilities, limitations, and reliability of generative AI systems. Clear communication, such as disclaimers or educational resources - ensures users understand when AI is producing or influencing content.
5. Intellectual Property Protection
Systems must include technical and organizational safeguards to prevent copyright infringement, plagiarism, or unauthorized use of protected works in both training data and generated outputs.
In 2024, Mexico’s Senate held public hearings on generative AI in elections and education, reflecting growing policy attention to these technologies.
Compliance obligations for businesses
The proposed Mexican AI law establishes clear compliance responsibilities across the AI value chain. Each actor, whether developing, deploying, or supporting AI systems, must adhere to defined duties based on their role and level of control. These obligations ensure accountability, data protection, and consistent governance across sectors.
| Entity Type | Main Duties |
|---|---|
| AI Developers | Implement risk assessments, maintain documentation, test and certify models. |
| AI Deployers | Ensure authorized systems only, maintain monitoring logs, and report incidents. |
| Data Processors | Protect training data per LFPDPPP and privacy principles. |
| Service Providers | Provide transparency disclosures to end users. |
Table 2. 0 Role-Based Compliance Obligations Under Mexico’s Proposed AI Law
Both domestic and foreign companies offering AI services to Mexican users must comply. Alignment with USMCA Digital Trade Chapter 19 obligations ensures interoperability and prohibits unjustified data-localization requirements.
Penalties and enforcement
Non-compliance carries administrative fines from 500 to 10 000 UMA (approx. €3 500 – €70 000), temporary suspension of AI operations, and potential criminal liability for severe violations affecting public safety. CONAIA will publish a registry of sanctioned entities to promote transparency.
Preparing for Mexico’s AI regulation
Key adaptation strategies:
- Compare current AI governance with the bill’s obligations.
- Focus on high-impact and high-risk systems.
- Involve legal, compliance, and engineering teams.
- Build staff awareness and accountability frameworks.
- Integrate ISO/IEC 42001 AI-management principles and NIST RMF 1.0.
Early compliance reduces costs, strengthens regulatory resilience, and enhances trust among clients and authorities.
Regional and global outlook
Mexico’s approach complements Latin American initiatives such as:
- Chile’s AI Bill (2024) – Risk-based oversight with ethical guidelines.
- Brazil’s PL 2338/2023 – Senate-approved framework emphasizing fundamental rights.
- Argentina’s AI Strategy 2025 – Non-binding governance guidelines.
Internationally, Mexico aligns with the OECD AI Principles and participates in UNESCO’s AI Ethics Recommendation, strengthening its role in global governance dialogues.
Implications for businesses
Compliance with Mexico’s forthcoming AI law can serve as a strategic differentiator rather than a constraint. Organizations that embed governance early will strengthen trust and brand reputation, demonstrating accountability to clients, regulators, and investors. Proactive adoption of transparency and oversight measures also mitigates liability and reputational risk, particularly for companies deploying high-risk or generative AI systems. Moreover, the law’s alignment with the EU AI Act and USMCA digital trade principles enables smoother regional expansion and cross-border data operations, creating a stable environment for responsible AI innovation and sustainable growth across Latin America.
Frequently Asked Questions
Who must comply?
All AI developers, deployers, and service providers operating in Mexico or serving Mexican users, regardless of physical location.
What are the core requirements?
Risk assessments, technical documentation, transparency, human oversight, and continuous monitoring for high-risk AI systems.
What are the penalties?
Fines up to 10 000 UMA, operational suspension, and potential criminal sanctions for severe harm.
How can companies prepare?
Conduct internal audits, implement AI governance systems, engage with CONAIA guidance, and align with ISO/IEC 42001 and NIST RMF 1.0.
Secure your AI future in Mexico’s evolving landscape
Mexico’s forthcoming AI law represents a transformative step for responsible innovation in Latin America. Organizations that act early, integrating risk management, transparency, and oversight, will gain a competitive advantage in a rapidly formalizing regulatory environment.
Nemko Digital assists businesses in building comprehensive AI-governance strategies, compliance frameworks, and risk-assessment systems aligned with Mexico’s evolving requirements.
Partner with our AI-compliance experts to ensure readiness for Mexico’s new regulatory era and lead the region in trustworthy AI adoption.
As Mexico prepares to finalize its AI law, proactive organizations that embed governance early will be best positioned to shape and thrive in Latin America’s emerging AI ecosystem.