AI Governance for Small States: Lessons from Armenia

Executive Summary

Artificial intelligence is no longer just a technology question. For small states, it is becoming a question of public administration, economic resilience, sovereignty, and trust.

Large countries can afford complex AI agencies, large testing infrastructure, and specialized regulators. Small states usually cannot. They often work with smaller public administrations, tighter budgets, fewer AI specialists, limited datasets, and weaker bargaining power with global technology providers.

But size is not only a weakness. Smaller states can also move faster, coordinate more easily, and test practical solutions before large bureaucracies can even agree on the process.

This article uses the term small states broadly. It includes Small Island Developing States and landlocked small states like Armenia with many similar challenges: a limited domestic market, dependence on imported technologies, talent constraints, and the need to connect AI adoption with national development.

The main argument is simple: small states do not need to copy the AI governance models of the United States, China, or the European Union. They need lighter, realistic, enforceable systems that help them use AI safely and productively. For Armenia, the next step is to connect its growing AI and digitalization agenda with practical rules on procurement, data protection, transparency, human oversight, civil-service training, and controlled experimentation through AI sandboxes.

1. Why AI Governance Matters for Small States

AI governance means the rules, institutions, standards, and everyday practices that determine how AI is developed, purchased, used, monitored, and corrected. In public administration, this is not a theoretical issue. It affects how citizens receive services, how institutions make decisions, and how governments build trust.

AI will increasingly appear in public services: chatbots, document review, tax-risk tools, health triage systems, education platforms, registry checks, and call centers. These tools can make government faster and more accessible. But if they are poorly designed or poorly supervised, they can also create bias, confusion, privacy risks, and unfair decisions.

Most small states will not build frontier AI models themselves. They will buy, adapt, or integrate systems developed elsewhere. That creates dependence on foreign vendors, cloud providers, foundation models, and digital infrastructure. Without proper procurement rules, governments may purchase systems they cannot audit, understand, or replace.

Small states are also underrepresented in global AI rule-making. The rules of AI are being shaped now, but many small states are not yet present in the main conversations. This is why platforms such as the Apply AI Alliance matter: they create space for policymakers, industry, academia, and civil society to discuss how AI should be applied in real sectors and real institutions.

For Armenia and similar countries, the question is not whether AI should be adopted. It already is being adopted. The real question is whether AI will be adopted with enough responsibility, transparency, and institutional capacity.

2. Why Small States Are Different

Small states face the same AI risks as larger countries, but they have fewer resources to manage them.

Their public administrations are smaller. A single ministry may be responsible for digitalization, telecommunications, innovation, cybersecurity, procurement, and public-sector transformation at the same time. This makes it difficult to build specialized AI regulators or large audit teams.

Budgets are also tighter. AI governance requires lawyers, engineers, data specialists, cybersecurity experts, procurement professionals, and trained public servants. These capacities are expensive to build, especially when the country also has urgent needs in education, healthcare, infrastructure, and security.

Talent is another challenge. Small states often train strong specialists, but many of them move to larger markets. This creates a gap between national AI ambitions and the actual number of experts available to implement them.

Data is also a problem. AI systems need quality data. In small states, data may be fragmented, outdated, paper-based, or too limited. For Armenia, this includes the need for Armenian-language datasets, sector-specific public data, and better data-sharing between institutions.

Finally, small states are strongly affected by rules made elsewhere. EU regulations, US technology standards, global cloud contracts, and the policies of large AI companies can shape what small states can realistically do. Even without a domestic AI law, Armenia and other small states are already living inside a global AI governance environment.

3. Armenia as a Small-State AI Governance Case

Armenia is a useful case because it is not starting from zero. It has a growing technology ecosystem, a strong engineering tradition, an active diaspora, and increasing attention to AI, digital infrastructure, and public-sector modernization.

The country already has important pieces of an AI ecosystem: digitalization priorities, AI infrastructure ambitions, access to high-performance computing, innovation platforms, and public-sector use cases. The next step is not only more technology. It is governance.

Armenia needs to connect AI development with the rules and habits that make AI trustworthy. Before adopting AI systems in government, institutions should ask practical questions:

• Who is responsible if the system gives wrong information?
• What data was used to train or fine-tune it?
• Can the system work properly in Armenian?
• Can citizens appeal decisions influenced by AI?
• Can the government leave a vendor contract without losing data or functionality?
• Are public officials trained enough to use the system responsibly?

These are not abstract legal questions. They are the difference between useful AI and risky automation.

4. Governance Priorities

Small states should not begin with overly complex AI legislation that they cannot enforce. A better approach is risk-based governance.

Low-risk systems may include internal document summarization, translation support, or public information chatbots. Medium-risk systems may support administrative workflows. High-risk systems include AI tools used in taxation, healthcare, education, law enforcement, public finance, social benefits, or digital identity.

The higher the risk, the stronger the obligations should be: documentation, testing, human oversight, audit logs, cybersecurity review, and appeal mechanisms.

AI governance also cannot work without data governance. Armenia should prioritize public-sector data quality, interoperability, anonymization, secure data-sharing, and Armenian-language datasets. The goal is not simply to collect more data, but to make data usable, lawful, secure, and relevant to local needs.

For small states, procurement is one of the strongest governance tools. Most AI systems used by the public sector will be purchased or adapted from vendors. Every AI procurement should require clear answers about data sources, model limitations, cybersecurity, explainability, bias testing, audit access, human oversight, and exit clauses. A system that cannot be audited, adapted, or replaced should not become critical public infrastructure.

Transparency also matters. Citizens should know when AI is used in public services. Public institutions should maintain an AI system register describing the purpose of each system, the responsible authority, the risk level, and the available safeguards. This does not mean publishing sensitive technical details. It means making sure citizens are not governed by invisible systems.

Finally, AI strategy should serve national development. For Armenia, the most practical areas include public services, agriculture, healthcare, education, climate resilience, cybersecurity, public finance, and SME productivity.

5. AI Sandboxes: The Practical Bridge Between Innovation and Regulation

For small states, AI governance should not begin with fear or overregulation. But it also should not begin with blind adoption. The most realistic middle path is the AI sandbox.

An AI sandbox is a controlled environment where government institutions, startups, universities, and technology providers can test AI systems before they are used at scale. It allows innovation to move forward, but under supervision, with clear rules, risk checks, documentation, and human accountability.

This is especially important for Armenia. The country has strong engineering talent, a growing technology ecosystem, and increasing interest in AI infrastructure. But it does not need to start by creating a heavy AI regulator modeled on much larger jurisdictions. What it needs first is a practical learning mechanism: a place where ministries and innovators can test AI tools, identify risks, correct mistakes, and build trust before systems are deployed in real public services.

In this sense, sandboxes are not only technical spaces. They are governance tools. They help small states learn how AI behaves in their own language, institutions, datasets, legal environment, and social context.

Sandboxes matter for four reasons.

First, they reduce the cost of mistakes. A small country cannot afford to deploy a public-sector AI system nationwide and only later discover that it gives inaccurate information, mishandles sensitive data, or performs poorly in the local language.

Second, they help governments understand technology before regulating it. Many public institutions are still learning what AI can and cannot do. A sandbox gives officials direct experience instead of forcing them to write rules based only on theory.

Third, they support startups and SMEs that cannot afford expensive compliance processes. A supervised environment gives smaller companies guidance, testing opportunities, and a clearer path to responsible deployment.

Fourth, they create evidence: what worked, what failed, what risks appeared, what safeguards helped, and what rules should be adjusted.

Armenia could create a National AI Sandbox for Public Value. It does not have to be a large institution. It could begin as a joint mechanism coordinated by the Ministry of High-Tech Industry, with participation from the Ministry of Justice, the Personal Data Protection Agency, cybersecurity authorities, universities, and selected sectoral ministries.

The first Armenian sandbox should focus on low- and medium-risk public-sector use cases:

• citizen information chatbots;
• document summarization for civil servants;
• Armenian-language translation and transcription tools;
• form-checking systems;
• agricultural advisory tools;
• environmental monitoring;
• public-service call center support;
• education tools;
• non-binding tax or customs risk indicators.

The key point is that these tools should be tested before they become official infrastructure. The sandbox should check whether the system works in Armenian, whether it gives reliable answers, whether it protects personal data, whether it can be explained, and whether a human official remains responsible.

For high-risk areas — healthcare, taxation, social benefits, law enforcement, digital identity, and public registries — Armenia should apply stricter sandbox rules. In these cases, AI should support human decision-making, not replace it.

6. Linking Armenia with the EU and US Through Sandboxes

Armenia should use AI sandboxes not only as a domestic tool, but also as a bridge to international cooperation.

The European Union is building one of the most structured AI sandbox systems in the world. Under the EU AI Act, Member States are expected to establish AI regulatory sandboxes that allow AI systems to be developed, trained, tested, and validated under regulatory supervision before they are placed on the market or put into service. Armenia is not an EU Member State, but it can still learn from this model and adapt it to its own needs.

This matters because Armenia is already associated with Horizon Europe, which gives Armenian organizations access to the EU's main research and innovation programme. That creates a practical opening for Armenian universities, startups, research centers, and public institutions to cooperate with European partners on trustworthy AI, testing, experimentation, digital governance, and sectoral AI applications.

Armenia can also connect with the EU's Testing and Experimentation Facilities, which support real-world AI testing in areas such as health, agrifood, manufacturing, and smart cities. For Armenian startups, this would be more than a technical opportunity. It would help them understand future European expectations on trustworthy AI, documentation, safety, and market readiness.

The United States offers a different but equally useful model. The US approach relies more on standards, testing, voluntary frameworks, measurement science, private-sector cooperation, and sector-specific oversight. For Armenia, this is valuable because a sandbox needs practical methods: checklists, metrics, benchmarks, red-teaming, cybersecurity testing, and model documentation.

NIST's AI Risk Management Framework can help Armenia build the technical side of sandboxing: how to map risks, measure them, manage them, and improve the trustworthiness of AI systems. Recent Armenia-US cooperation on AI, semiconductors, cybersecurity, workforce development, and trusted digital infrastructure creates a political opening for a practical AI sandbox cooperation track.

A useful Armenian model could therefore combine three elements: EU-aligned governance, US-informed testing, and Armenian public priorities.

From the EU, Armenia can adapt risk categories, data protection, transparency, human oversight, and regulatory learning. From the US, it can learn technical evaluation, risk management, red-teaming, cybersecurity testing, and pro-innovation public-private cooperation. From its own context, Armenia can define the real purpose: Armenian-language AI, better public services, agriculture, healthcare, education, cybersecurity, digital identity, and stronger public administration.

This would allow Armenia to position itself not as a passive recipient of foreign technology, but as a small-state testing ground for responsible, public-value AI.

7. Priority Sandbox Areas for Armenia

The first Armenian AI sandbox should not start with the most sensitive systems. It should begin where social value is high and implementation risk is manageable.

Public-service information assistants

These tools can help citizens understand procedures, documents, deadlines, and service requirements without making final decisions about their rights.

Agriculture

AI can support crop monitoring, pest detection, irrigation advice, climate-risk alerts, and market information. This is especially relevant for Armenia, where agriculture remains socially and economically important.

Healthcare administration

Healthcare should begin with administration rather than automated diagnosis. Appointment management, resource planning, document processing, and patient information support are safer first steps.

Education

Education is suitable for controlled experimentation: teacher support, Armenian-language learning tools, AI literacy modules, and safe classroom assistants.

Environment and disaster response

Environmental monitoring and disaster response can benefit from AI tools for wildfire prediction, air-quality monitoring, water-resource planning, and emergency communication.

SME support

AI can support SMEs by helping small businesses understand tax rules, export requirements, grant applications, and digital tools. This would connect AI adoption directly with economic development.

8. Risks and Safeguards

The biggest AI risks for small states are not only technical. They are institutional.

AI systems trained mainly on foreign data may not understand Armenian language, names, local administrative categories, rural conditions, or social realities. This can create bias and poor performance.

Vendor lock-in is another major risk. If the state becomes dependent on closed systems, it may lose control over costs, data, and long-term policy choices.

Privacy and cybersecurity must also be central. AI systems often process sensitive data. Data protection should therefore be part of procurement and deployment from the beginning, not added later.

Small states also face infrastructure dependency. Cloud and compute partnerships are useful, but governments must understand where data is stored, which law applies, who can access it, and what happens if the service becomes too expensive or unavailable.

Finally, small states may lack audit capacity. This is why regional cooperation, shared testing tools, university partnerships, and international networks are so important.

9. How Small States Should Cooperate

Small states should not try to solve AI governance alone. They can cooperate by sharing procurement templates, risk-classification models, audit methods, public-sector training materials, and lessons from failed or successful pilots.

They can also create regional or cross-regional AI sandboxes. One country may test AI in agriculture, another in public-service chatbots, another in disaster response, and another in healthcare administration. Results can then be shared.

Small states should also negotiate together when dealing with major technology vendors. A single small market has limited bargaining power. A group of small states can ask for better terms on data residency, pricing, local language support, audit access, and knowledge transfer.

This is where Armenia can contribute. Armenia has a strong technology talent base and can become a useful bridge between small-state governance needs, regional cooperation, and practical AI applications in sectors such as agriculture, healthcare, public administration, and digital governance.

10. Policy Recommendations

1. Adopt a lightweight but enforceable AI governance framework

Armenia should not wait for a perfect AI law. It can begin with a government decision, public-sector AI policy, or executive framework that defines risk categories, prohibited uses, procurement rules, transparency requirements, and human-review obligations.

2. Create a public-sector AI system register

Every ministry and agency using AI should register the system, purpose, vendor, data categories, risk level, responsible official, safeguards, and appeal mechanism. Sensitive technical details can remain confidential, but the existence and purpose of AI systems should be visible.

3. Build AI procurement rules

Public procurement should require model documentation, cybersecurity review, data-protection assessment, bias testing, audit logs, explainability, human oversight, Armenian-language performance testing, and exit clauses.

4. Establish an Armenian AI Sandbox for Public Value

Armenia should create a national sandbox to test public-sector and public-interest AI systems before full deployment. The sandbox should bring together government, startups, universities, civil society, data protection authorities, cybersecurity experts, and international partners. Its first use cases should focus on low- and medium-risk areas such as public-service information, agriculture, education, healthcare administration, environmental monitoring, and SME support.

5. Build a simple safety habit: think before, learn after, never leave AI on autopilot

Before an AI system is introduced, public bodies should ask what could go wrong. After a failure, they should identify the real cause. In high-impact services, AI should never become the final decision-maker. Humans must remain responsible, and citizens must retain the right to explanation, review, and appeal.

6. Fund AI literacy and civil-service training

Civil servants do not all need to become engineers, but they must understand AI risks, data protection, procurement questions, cybersecurity, and responsible use. Training should begin with officials in procurement, legal departments, service delivery, health, education, tax, and social services.

7. Prioritize high-value, low-risk use cases

Armenia should begin with AI tools that improve service quality without deciding citizens' rights: information assistants, document summarization, form checking, backlog management, agricultural advisory tools, environmental monitoring, and internal analytics.

8. Cooperate with peer small states and international platforms

Armenia should work with small-state networks, UN bodies, universities, diaspora experts, trusted technology partners, and forums such as the Apply AI Alliance community. The aim should be practical knowledge exchange: what works, what fails, and what safeguards are needed.

9. Link AI governance to national development

AI should not be adopted because it is fashionable. It should support concrete national goals: better public services, stronger agriculture, improved healthcare, education modernization, environmental resilience, cybersecurity, and more efficient public finance.

Conclusion

Small states do not need the scale of large powers to govern AI effectively. They need clarity, coordination, trusted data, practical safeguards, and partnerships.

Armenia's experience shows both the opportunity and the challenge. The country is moving toward AI infrastructure, innovation platforms, high-performance computing, and AI-enabled public services. The next stage should be governance: risk-based rules, public-sector AI registers, procurement standards, human oversight, data protection, practical training, and sandbox-based experimentation.

The most viable model for Armenia, and for other small states facing similar constraints, is adaptive, regional, and development-oriented AI governance. But this model should be built through practice, not only through strategy papers. AI sandboxes can become the bridge between ambition and implementation. They allow Armenia to test AI safely, learn from mistakes, cooperate with EU and US partners, support local innovators, and build public trust before AI systems are used at scale.

Armenia should welcome foreign technology, but not dependency; encourage innovation, but not autopilot; and use AI to strengthen public institutions, not to replace their responsibility to citizens.

Sources and Further Reading

• Apply AI Alliance — European Commission
• Apply AI Strategy — European Commission
• EU AI Act Service Desk — Article 57 on AI regulatory sandboxes
• Horizon Europe — Armenia association
• EU Testing and Experimentation Facilities
• NIST AI Risk Management Framework
• Armenia-US Strategic Partnership Charter — Ministry of Foreign Affairs of Armenia
• Joint Statement on AI Opportunity Partnership — U.S. Department of State