AI for National Security: Building Sovereign Intelligence Infrastructure

Why Relying on Foreign AI Systems Is a Strategic Vulnerability --- And How to Build Secure, Self-Hosted Intelligence Capabilities

by Adverant Research Team November 2025

Idea in Brief

The Challenge Nation-states are racing to deploy AI across intelligence, defense, and border security operations---but most are building on infrastructure they don't control. Foreign cloud dependencies, opaque AI supply chains, and geopolitical tensions create five critical vulnerabilities that threaten national security.

The Opportunity Air-gapped, self-hosted AI systems---operating on classified networks isolated from the internet---enable intelligence agencies and militaries to leverage frontier AI models without exposing sensitive data to adversaries. Recent deployments by the NSA, Pentagon, and NATO demonstrate this is not just possible but operationally necessary.

The Path Forward Building sovereign AI capabilities requires a coordinated strategy across three dimensions: technical infrastructure (secure compute, air-gapped systems, domestic data centers), organizational transformation (new procurement models, talent pipelines, cross-agency collaboration), and geopolitical positioning (technology alliances, export controls, standards-setting). Nations that succeed will maintain decision superiority in an AI-enabled world. Those that fail risk becoming digital dependencies of foreign powers.

When Microsoft announced in May 2024 that it had deployed GPT-4 on the Azure Government Top Secret cloud---the first-ever instance of a frontier AI model running on classified networks---it marked a watershed moment for national security. After 18 months of intensive work, U.S. intelligence agencies and military commands could finally access generative AI capabilities without their data ever leaving air-gapped, government-controlled infrastructure.

The significance extends far beyond a single technical achievement. It represents a fundamental answer to the most pressing question facing defense and intelligence leadership worldwide: How do we harness AI's transformative potential without creating catastrophic security vulnerabilities?

The answer matters because the stakes are existential. AI is rapidly becoming the substrate of intelligence operations, military decision-making, and border security. The NSA uses AI to identify foreign hackers attempting to breach U.S. critical infrastructure. U.S. Customs and Border Protection has processed more than 193 million travelers using AI-powered biometric facial comparison, identifying over 1,500 individuals using false identities. The Pentagon's Chief Digital and Artificial Intelligence Office oversees more than 685 AI-related projects, several tied to major weapons systems.

Yet most of these systems rest on a precarious foundation. According to Bain & Company's 2024 analysis, Nvidia projected $10 billion in revenue from governments' sovereign AI investments in 2024---up from zero the previous year. Why the sudden urgency? Because intelligence and defense leaders are waking up to an uncomfortable reality: relying on foreign AI infrastructure isn't just a technical dependency. It's a strategic vulnerability that adversaries are actively preparing to exploit.

The Five Critical Vulnerabilities of Foreign AI Dependence

The risks of building national security capabilities on foreign AI infrastructure cluster into five distinct threat vectors, each with documented evidence of exploitation or vulnerability.

1. Supply Chain Infiltration and Backdoor Access

The most direct threat comes from compromised hardware and software in the AI stack. In 2012, the U.S. House Intelligence Committee concluded that Chinese telecommunications companies Huawei and ZTE posed national security threats, warning that their equipment could "undermine core U.S. national security interests." A decade later, an FBI investigation found that Huawei equipment could disrupt U.S. military communications, including those about the nuclear arsenal.

The legal framework makes this threat concrete. Article 7 of China's National Intelligence Law requires all "organizations and citizens" to "provide support and assistance to and cooperate with the State intelligence work." Article 17 allows Chinese intelligence agencies to take control of an organization's facilities, including communications equipment. In 2020, FCC Chairman Pai explicitly stated that both companies "have close ties to the Chinese Communist Party and China's military apparatus, and both companies are broadly subject to Chinese law obligating them to cooperate with the country's intelligence services."

This isn't hypothetical. In February 2020, the Wall Street Journal reported that U.S. officials claimed Huawei has had the ability to covertly exploit backdoors in carrier equipment since 2009. Former NSA Director Michael Hayden claimed he had seen "hard evidence of backdoors in Huawei's networking equipment." The National Defense Authorization Act now specifically bans five Chinese technology companies---Hikvision, Dahua, Huawei, ZTE, and Hytera---from supplying systems to federal agencies.

For AI systems, the attack surface is even larger. Atlantic Council research on AI supply chain security documents that "data poisoning, training framework vulnerabilities, and model tampering are significant threats to AI models." If AI components originate from adversarial nations, they could carry vulnerabilities that compromise not only system integrity but the broader security of critical sectors.

2. Data Sovereignty and Intelligence Leakage

Every query sent to a cloud-based AI model is a potential intelligence leak. When intelligence analysts use commercial AI services to process classified information---even inadvertently---they risk exposing operational details, investigative methods, and sensitive sources.

This risk became operational reality in 2024 when the U.S. Army blocked the Air Force's generative AI chatbot, NIPRGPT, from its networks, citing cybersecurity and data governance concerns. Despite being developed by the Air Force Research Laboratory specifically for military use, the Army flagged the program as risky and shut it down across Army networks. If inter-service AI deployments face this level of scrutiny, the risks of using foreign commercial AI services for classified work are orders of magnitude higher.

The scale of potential exposure is staggering. Intelligence agencies analyze millions of intercepted communications, satellite images, and signals intelligence reports daily. Each query to a foreign AI service could leak metadata about collection priorities, analytical focus areas, or investigative targets. Over time, these patterns reveal strategic intentions and operational capabilities.

According to Lawfare's analysis of sovereign AI strategies, "many countries participating in China's Belt and Road initiative are adopting Chinese-built AI systems for smart cities and government operations. These systems come with long-term dependencies, enabling Beijing to exert political and economic influence on a global scale."

3. Geopolitical Coercion and Kill-Switch Scenarios

Dependence on foreign AI infrastructure creates leverage points for coercion during geopolitical crises. If a nation's intelligence analysis, military decision-making, or border security depend on cloud services hosted in another country, that dependency becomes a weapon.

The scenario isn't hypothetical. Europe's dependence on U.S. cloud infrastructure is already seen by many on the continent as a strategic vulnerability. Despite efforts like the €200 billion EU AI Continent Action Plan and the GaiaX program to build European alternatives, this dependency persists. As Brookings Institution research notes, "Europe's dependence on US cloud infrastructure is seen by many on the continent as a strategic vulnerability."

The infrastructure gap is daunting. In just the first six months of 2024, Microsoft, Amazon, Google, and Meta collectively spent more than $100 billion on AI and broader cloud infrastructure. In contrast, the EuroHPC project with the largest budget is only 2 percent of that amount---roughly $2 billion divided among potentially dozens of initiatives.

For nations with adversarial relationships with the U.S. or China, the threat is even more acute. Export controls on advanced AI chips, already implemented by the U.S. to restrict China's access to frontier capabilities, demonstrate how AI infrastructure can be weaponized. The U.S. has divided the world into three groups for AI chip sales, ranging from least to most restricted---a de facto technology alliance system that forces nations to choose sides.

4. Training Data Poisoning and Model Manipulation

The integrity of AI models depends entirely on the integrity of their training data. For national security applications, this creates a unique vulnerability: adversaries can manipulate AI behavior by poisoning training datasets or compromising model training processes.

According to cybersecurity research documented by OpenText, over 75% of software supply chains were attacked in the last 12 months. Among those experiencing ransomware attacks, 62% reported impacts from attacks originating from software supply chain partners.

For AI systems, these attacks can be subtle and persistent. An adversary with access to training infrastructure could inject biased data that causes models to misclassify threats, overlook specific attack patterns, or generate subtly incorrect intelligence assessments. Unlike traditional software backdoors, model poisoning is difficult to detect and may only manifest under specific conditions that adversaries can trigger.

The World Economic Forum's Global Cybersecurity Outlook 2025 found that 72% of respondents reported a rise in cyber risks, with "AI-enhanced tactics---such as phishing, vishing and deepfakes---and a notable increase in supply chain attacks." For intelligence and defense applications, these aren't just cybersecurity concerns---they're mission-critical vulnerabilities.

5. Talent and Capability Drain

Perhaps the most insidious vulnerability is the gradual erosion of domestic AI capabilities. When nations rely on foreign AI services, they lose the imperative to develop indigenous expertise. Over time, this creates a permanent dependency where the nation lacks not just the infrastructure but the human capital to build its own systems.

France's national AI strategy explicitly warns against this risk, cautioning that France and European states risk becoming "cybercolonies" of the U.S. and China. The concern isn't just about current capabilities but future optionality. Nations that cede AI development to foreign powers will find it increasingly difficult to chart independent strategic courses.

The Chatham House analysis of the U.S.-China AI race notes that "rising tensions between the U.S. and China, alongside fears of being left behind in the AI race, have spurred governments from Seoul to São Paulo to prioritize sovereign AI---the ability to produce AI with their own data, infrastructure, workforce, and networks---which officials say is critical to national security."

Why Air-Gapped AI Is Critical for National Security Operations

The solution to foreign AI dependencies isn't to avoid AI altogether---that path leads to strategic obsolescence. Instead, intelligence and defense organizations are embracing air-gapped AI systems: frontier models deployed on physically isolated, classified networks that never connect to the public internet.

Air-gapping isn't a new concept in national security. Classified networks have been physically isolated from unclassified networks for decades. What's new is the ability to run state-of-the-art AI models on these networks at operational scale.

The Microsoft Azure Government Top Secret Breakthrough

Microsoft's deployment of GPT-4 on Azure Government Top Secret represents the first instance of a frontier generative AI model operating in a fully classified environment. As reported by Nextgov, the effort took approximately 18 months and required establishing "a new instance of ChatGPT specifically for classified U.S. government workloads."

The architecture ensures absolute data isolation. End users on the Department of Defense's classified network can access the generative AI toolkit for various uses, but they cannot train the model on new information because the model is completely air-gapped. Data never leaves the classified environment. There is no connection to Microsoft's commercial cloud infrastructure. No queries leak to external servers.

This approach solves the data sovereignty challenge entirely. Intelligence analysts can use frontier AI capabilities to process satellite imagery, analyze intercepted communications, or correlate threat intelligence without any risk of data exfiltration. The model operates entirely within the government's security boundary.

NATO's Partnership with Google Cloud

NATO's approach demonstrates how alliances can extend sovereign AI capabilities across trusted partners. In 2024, NATO's Communications and Information Agency (NCIA) gained access to Google Distributed Cloud (GDC)---including Google's air-gapped, fully isolated cloud platform designed for classified workloads.

The platform enables defense organizations to run analytical and AI-heavy workloads inside completely disconnected environments, ensuring that data never leaves NATO's sovereign perimeter. This architecture allows NATO member states to share AI capabilities and threat intelligence while maintaining strict national security controls.

The Pentagon's $800 Million AI Investment

The scale of U.S. commitment to secure AI deployment became clear in 2025 when the Pentagon's Chief Digital and Artificial Intelligence Office (CDAO) awarded nearly $800 million in contracts to four leading AI firms: OpenAI, Anthropic, Google Public Sector, and xAI. The contracts specifically focused on deploying frontier AI models for warfighting, intelligence, and enterprise modernization.

Google Public Sector's contract emphasizes supplying "high-performance computing infrastructure and its secure, air-gapped cloud environments tailored for classified data handling at the highest levels." This isn't experimental---it's operational deployment at scale.

The Pentagon's broader AI infrastructure, the Joint Warfighting Cloud Capability (JWCC), represents a $9 billion program with four vendors: Google, Oracle, Amazon Web Services, and Microsoft. The multi-cloud approach provides resilience and prevents single points of failure while enabling different classification levels and mission requirements.

Real-World Intelligence Applications

The operational value of air-gapped AI is already documented across intelligence agencies.

The NSA established the AI Security Center (AISC) in September 2023 to "oversee the development and integration of artificial intelligence capabilities within U.S. national security systems." The center's mission explicitly includes detecting and countering AI vulnerabilities in national security infrastructure.

NSA's Cybersecurity Division Director Rob Joyce acknowledged in 2024 that NSA uses AI to identify hackers attempting to breach U.S. critical infrastructure, specifically using machine learning to trace Chinese hacks aimed at U.S. transportation networks, pipelines, and ports. These operations require processing highly classified signals intelligence on secure systems.

The CIA is similarly integrating AI across operations. CIA CIO La'Naia Jones noted that "AI is now being looked at in every aspect and facet of, not just CIA, but really across the intel community," including generative AI for both business operations and the agency's open-source enterprise.

Both CIA and NSA have been "moving capabilities into the Amazon cloud for about three to four years," using cloud infrastructure as the foundation for both AI and generative AI tools---all within classified environments.

Border Security: CBP's AI Deployment at Scale

U.S. Customs and Border Protection demonstrates how AI can enhance security operations while processing millions of interactions. As of July 2022, CBP had deployed facial recognition technology to 32 airports for travelers leaving the U.S. and all airports for travelers entering the country.

The scale is remarkable. More than 193 million travelers have been processed using biometric facial comparison technology, allowing CBP to biometrically confirm more than 1,500 individuals posing under false or assumed identities. The system maintains a minimum accuracy rate of 97%, with operational data showing no demographic performance differences.

CBP uses AI not just for identity verification but for threat detection at the border. AI models automatically identify objects in streaming video and imagery, sending real-time alerts to operators when anomalies are detected. This enhances CBP's ability to stop drugs and illegal goods from entering the country while processing legitimate travelers efficiently.

These systems handle extraordinarily sensitive data---biometric information, travel patterns, law enforcement alerts. Operating them on air-gapped or highly secured networks prevents adversaries from accessing immigration enforcement intelligence, identifying intelligence personnel through travel patterns, or mapping U.S. security infrastructure.

Building Sovereign AI Capabilities: A Technical and Organizational Roadmap

Recognizing the strategic necessity of sovereign AI is the first step. Building the capabilities requires a coordinated effort across technical infrastructure, organizational transformation, and geopolitical positioning. Based on documented approaches from the U.S., European allies, and other nations, here's a comprehensive roadmap.

Phase 1: Establish Foundational Infrastructure (0-18 Months)

Secure Compute at Scale

The foundation of sovereign AI is domestic compute infrastructure capable of training and running frontier models. This requires high-performance computing clusters with specialized AI accelerators (GPUs or TPUs) and the energy infrastructure to power them.

France is leading Europe on this dimension. The French Armed Forces Ministry announced plans to build Europe's most powerful classified supercomputer to support defense-oriented AI development and deployment. This follows France's 2019 strategy "AI in service of defense," which outlined ethical frameworks, infrastructure development, and research priorities.

The U.S. approach leverages the $9 billion Joint Warfighting Cloud Capability, which provides multiple classification levels across four cloud vendors. The Pentagon is also investing in dedicated AI sandboxes---$20 million for Compute and AI Sandboxes launching in early 2025---enabling AI testing and experimentation on government networks.

Nations building sovereign AI infrastructure should:

Assess current compute capacity across defense, intelligence, and government agencies
Identify gaps between current infrastructure and requirements for frontier AI models
Prioritize domestic data centers on secure networks over commercial cloud dependencies
Plan for massive scale: Training large language models requires thousands of GPUs
Secure energy infrastructure: AI compute is energy-intensive; plan for dedicated power

Air-Gapped Network Architecture

Classified networks must be physically isolated from the internet while enabling collaboration across authorized agencies and allies. This requires careful network design that balances security with operational effectiveness.

According to government IT specialists at GitLab, "classified networks are physically isolated from unclassified networks and are extremely GPU-constrained. Training ML models is highly GPU-intensive, so training in GPU-constrained networks can lead to serious challenges."

The solution is a tiered architecture:

High-side classified networks: For most sensitive operations, completely air-gapped
Medium-security enclaves: For less-sensitive but still controlled workloads
Low-side testing environments: For validating AI tools before classified deployment

NATO's approach with Google Distributed Cloud demonstrates this architecture. The platform operates inside completely disconnected environments while maintaining the capability to run analytical and AI-heavy workloads.

Data Sovereignty and Storage

AI models are only as good as their training data. Sovereign AI requires control over both the data used to train models and the data processed during inference.

Nations should:

Audit current data dependencies: Identify where sensitive data is stored and processed
Repatriate critical datasets: Move intelligence, defense, and border security data to domestic infrastructure
Establish data governance frameworks: Define who can access what data under what circumstances
Create secure data-sharing protocols: Enable allied collaboration without compromising sovereignty

Phase 2: Develop Indigenous AI Capabilities (12-36 Months)

Talent Pipeline and Skills Development

Sovereign AI requires domestic expertise in AI research, engineering, and operations. This means competing with commercial tech companies for scarce AI talent.

The Pentagon's approach includes $40 million for Innovative GenAI Solutions through Small Business Innovation Research (SBIR) funding, explicitly designed to build a domestic AI industry serving national security needs.

Germany's Cyber Innovation Hub of German Armed Forces (CIHBw) and France's Ministerial Agency for Artificial Intelligence in Defence (AMIAD) represent organizational structures dedicated to AI deployment in defense, combining military requirements with technical expertise.

Successful talent strategies include:

Competitive compensation: Match or exceed private sector salaries for critical AI roles
Mission-driven recruitment: Emphasize national security impact over commercial applications
University partnerships: Fund AI research with national security applications
Rotation programs: Enable movement between academia, industry, and government
Clearance pipelines: Streamline security clearance processes for AI specialists

Model Development and Adaptation

While leveraging commercial AI models on secure infrastructure (like Microsoft's GPT-4 deployment) provides immediate capability, long-term sovereignty requires the ability to develop and fine-tune models domestically.

Europe is pursuing this through initiatives like Helsing, a German defense AI company founded in 2021, which reached a €12 billion valuation after raising €600 million. Helsing partners with France's Mistral AI to develop AI defense systems specifically for European requirements.

The AI Rapid Capabilities Cell announced by the Pentagon in December 2024 focuses on exactly this challenge: adapting frontier AI models to warfighting and enterprise management use cases. The cell launched four Frontier AI pilots applying generative AI to specific defense missions.

Key steps for model development:

Start with adaptation, not training from scratch: Fine-tune existing models on classified data
Build evaluation frameworks: Develop metrics for assessing model performance on national security tasks
Create secure ML pipelines: Establish processes for training models on classified networks
Invest in smaller, specialized models: Not all applications require frontier-scale models
Document and validate: Ensure models can be audited and their decisions explained

Procurement Reform and Vendor Management

Traditional defense procurement processes are too slow for the AI era. The technology changes faster than multi-year acquisition cycles can adapt.

The Pentagon's approach through the Defense Innovation Unit and the AI Rapid Capabilities Cell represents a new model: rapid prototyping, quick vendor selection, and iterative deployment. The $800 million in contracts to OpenAI, Anthropic, Google Public Sector, and xAI demonstrates this agility.

Procurement reforms should include:

Modular contracting: Break large programs into smaller, faster procurement cycles
Commercial partnerships: Leverage commercial AI innovation while maintaining security requirements
Rapid prototyping authority: Enable agencies to test technologies before full-scale procurement
Security-first vendor evaluation: Assess not just capability but supply chain security
Allied vendor preferences: Prioritize vendors from allied nations over potential adversaries

Phase 3: Operational Integration and Alliance Building (24-60 Months)

Cross-Agency Coordination

AI capabilities must flow across organizational boundaries to be effective. The Pentagon's CJADC2 (Combined Joint All-Domain Command and Control) initiative demonstrates this imperative. Deputy Secretary of Defense Kathleen Hicks stated in 2024 that "the minimum viable capability for CJADC2 is real and ready now," describing it as "low latency and extremely reliable."

CJADC2's success required creating a "completely vendor-agnostic data integration layer," making data extensible across various operational systems. This same approach applies to AI: models and data must be accessible across intelligence agencies, military commands, and homeland security operations without compromising security.

Effective cross-agency AI coordination requires:

Common data standards: Enable information sharing across agency boundaries
Federated learning approaches: Allow agencies to collaborate on model training without sharing raw data
Joint AI operations centers: Create coordination mechanisms for multi-agency AI deployment
Shared threat intelligence: Pool AI-derived insights about adversary capabilities
Unified governance: Establish clear decision-making authority for AI deployments

Allied Collaboration and Technology Alliances

No single nation---not even the United States---can achieve complete AI sovereignty alone. Even the U.S. depends on Taiwan's TSMC for nearly all of Nvidia's chip production. As Brookings Institution research notes, "maximalist visions of AI sovereignty are not realistic---not for Europe, and not for any country or region, including the United States."

The solution is technology alliances among trusted partners. NATO's adoption of Google Distributed Cloud demonstrates this model. European defense collaborations like the Future Combat Air System (FCAS)---involving France, Germany, and Spain developing AI-enabled fighter teaming between manned fighters and autonomous drone swarms---show how allies can pool resources.

The Tempest program (UK and Italy), a sixth-generation aircraft planned for 2035 deployment with extensive AI-enabled technologies, represents another allied approach to developing capabilities that might be out of reach for single nations.

Strategic alliance considerations:

Five Eyes coordination: Leverage existing intelligence-sharing frameworks (US, UK, Canada, Australia, New Zealand)
EU defense integration: Support European efforts to reduce dependency on non-allied technology
NATO AI standards: Develop shared security standards and interoperability requirements
Technology transfer agreements: Enable allied nations to share AI capabilities
Joint R&D programs: Pool resources for expensive frontier AI research

Continuous Testing and Red-Teaming

AI systems face evolving threats. Biden's October 2024 National Security Memorandum on AI directs the NSA, through its AI Security Center, to "develop the capability to perform rapid systematic classified testing of AI models' capacity to detect, generate, and/or exacerbate offensive cyber threats" within 120 days.

This testing-first approach should be standard across all sovereign AI deployments. The NSA, CISA, and FBI joint guidance on deploying AI systems securely provides a starting framework, but continuous adaptation is essential.

Testing protocols should include:

Adversarial testing: Simulate attacks from nation-state adversaries with sophisticated capabilities
Model robustness evaluation: Test how models perform under data poisoning or adversarial inputs
Supply chain audits: Regularly verify the integrity of hardware and software components
Operational security exercises: Test whether AI deployments leak information through side channels
Resilience validation: Ensure AI systems can operate during network disruptions or attacks

The Geopolitical Implications of AI Sovereignty

The race to build sovereign AI capabilities is reshaping global power structures as profoundly as nuclear weapons did in the mid-20th century. But unlike nuclear weapons, where proliferation could be controlled through international agreements, AI capabilities are diffusing rapidly---and unevenly---across nations and alliances.

The U.S.-China Technology Bifurcation

As Chatham House analysis reveals, "today, the U.S. and China dominate the full AI stack, leaving the rest of the world with a difficult implicit choice: align with one version of the stack or sit on the fence between the two."

This bifurcation is accelerating. The U.S. has implemented chip export controls that divide the world into three groups for AI chip sales, from least to most restricted. China has responded by reportedly stopping participation in the Top500 supercomputer list to maintain secrecy of its supercomputing advancements.

China promotes its infrastructure through the Digital Silk Road and its Global AI Governance Action Plan, extending influence through BRICS nations. Beijing released some of the world's earliest and most comprehensive guidelines and regulations for AI services, positioning itself as a regulatory leader.

For U.S. allies and partners, this creates difficult choices. Aligning fully with U.S. technology risks economic and diplomatic tension with China. Adopting Chinese AI systems risks both technical dependence on Beijing and friction with Washington. Most nations are attempting to chart a middle course---building indigenous capabilities while maintaining relationships with both powers.

Europe's Struggle for Strategic Autonomy

Europe represents the clearest test case of whether advanced economies can build genuine AI sovereignty outside the U.S.-China duopoly.

The €200 billion EU AI Continent Action Plan and additional $150 billion from the Security Action for Europe (SAFE) instrument demonstrate the scale of European ambition. These funds target common procurement of defense capabilities and building AI infrastructure.

Yet the challenges are formidable. U.S. Big Tech companies collectively made over $1.5 trillion in revenue in 2024 and plan to invest up to $320 billion on AI technologies in 2025. Europe's investments, while substantial, operate at a different order of magnitude.

Success stories do exist. Germany's Helsing, partnering with France's Mistral AI, shows that European companies can compete in defense AI. France's commitment to building Europe's most powerful classified supercomputer demonstrates technical capability.

But as the European Council on Foreign Relations notes, "the United States dominates in defence AI thanks to deep integration between tech companies and the Department of Defense." Europe has yet to achieve similar integration between its tech sector and defense establishment.

The ultimate test is whether Europe can escape what France's national AI strategy calls "cybercolony" status---a future where European nations use AI infrastructure but don't control it.

The Sovereignty-as-a-Service Trap

Recognizing nations' sovereignty concerns, major tech companies have responded with "sovereignty-as-a-service" offerings. Nvidia has made deals with Thailand, Vietnam, and the UAE. Microsoft has agreements with the UAE and others. Amazon Web Services offers a European "sovereign cloud."

These offerings provide real benefits---nations gain access to frontier AI capabilities without building everything from scratch. But they also create subtle dependencies. If the underlying technology, model architectures, and training techniques remain foreign, has sovereignty truly been achieved?

The answer depends on the specific implementation. Microsoft's Azure Government Top Secret, where the model runs entirely on U.S. government infrastructure with no external connectivity, represents genuine sovereignty. Commercial cloud services with data residency guarantees offer less---the data may reside domestically, but the platform, algorithms, and ultimate control remain foreign.

Nations must carefully evaluate sovereignty-as-a-service offerings:

Where does data actually reside? Data residency doesn't equal data sovereignty if foreign entities can access it.
Who controls the encryption keys? If the vendor holds keys, they can access data regardless of residency.
Can the service be shut off remotely? True sovereignty means the system continues operating even if relations with the vendor deteriorate.
Is domestic expertise being developed? Or is the nation becoming more dependent over time?
What happens if geopolitical tensions escalate? Will the vendor honor contracts if governments pressure them?

The National Security Imperative

The geopolitical implications ultimately converge on a single point: AI sovereignty is not optional for nations that aspire to strategic independence.

A nation that depends on foreign AI for intelligence analysis cannot protect its sources and methods. A military that relies on foreign AI for targeting and decision-making cannot guarantee those systems will function during conflict. A government that processes citizen data through foreign AI cannot ensure that information won't be used against national interests.

This doesn't mean every nation needs to replicate Google or OpenAI domestically. It means every nation must answer three questions:

What AI capabilities are mission-critical to national security? Focus sovereignty efforts there.
Which allied nations share security interests? Build collaborative sovereignty with them.
What level of foreign dependency is acceptable? Be explicit about trade-offs and risk tolerance.

For intelligence agencies, the answer is clear: foreign dependencies in AI systems that process classified information are unacceptable. For military applications, the answer is equally clear: autonomous weapons and command-and-control systems must operate on sovereign infrastructure. For border security and law enforcement, the calculus is more nuanced but still points toward minimizing foreign dependencies.

What Leaders Should Do Now

The good news is that building sovereign AI capabilities is demonstrably achievable. The U.S., France, and allied nations have already deployed frontier AI models on classified networks. The infrastructure exists. The partnerships are forming. The question isn't whether it's possible---it's whether your organization is moving fast enough.

For National Security Leaders

Immediate Actions (Next 90 Days):

Conduct a comprehensive AI dependency audit. Map every AI system currently in use across intelligence, defense, and homeland security. Identify foreign dependencies, data flows, and security boundaries. Classify systems by criticality and risk exposure.
Establish an AI sovereignty task force. Bring together technical experts, operational leaders, procurement specialists, and policy makers. Give them authority to make recommendations that cross organizational boundaries. Set a 180-day timeline to produce an implementation roadmap.
Engage with allied nations on AI collaboration. Don't wait for formal government-to-government agreements. Start technical-level discussions with counterparts in allied nations about shared AI infrastructure, common security standards, and joint development programs.
Test air-gapped AI immediately. Don't wait for perfect solutions. Partner with vendors like Microsoft, Google, or Amazon who already have classified cloud capabilities. Run pilot programs on non-sensitive workloads to build organizational familiarity.

Strategic Initiatives (Next 12-24 Months):

Invest in domestic compute infrastructure. Budget for high-performance computing clusters on classified networks. This is expensive---expect costs in the hundreds of millions to low billions---but the strategic value far exceeds the financial investment. France's classified supercomputer and the Pentagon's JWCC provide models.
Reform procurement to enable AI-era agility. Traditional multi-year acquisition cycles are incompatible with AI's pace of change. Implement modular contracting, rapid prototyping authorities, and commercial partnership frameworks. The Defense Innovation Unit and AI Rapid Capabilities Cell demonstrate what's possible.
Build the talent pipeline. You cannot hire your way to AI sovereignty---there aren't enough cleared AI specialists. Instead, create rotation programs that move people between academia, industry, and government. Fund university research with security applications. Make government AI work mission-driven and technically challenging.
Develop national AI security standards. Work with allies through NATO, Five Eyes, or regional partnerships to establish common security baselines for AI systems. This enables interoperability while preventing a race to the bottom on security requirements.

For Intelligence Agency Leadership

Immediate Actions:

Catalog all uses of commercial AI services. Many analysts are already using ChatGPT, Claude, or other commercial AI tools---often through personal accounts on unclassified systems. Document current usage, identify security risks, and provide approved alternatives.
Deploy air-gapped AI for open-source intelligence (OSINT) first. OSINT provides a lower-risk testing ground for AI tools. You can validate efficacy on unclassified "low side" systems, then go through rigorous vetting to bring them to classified "high side" systems. The intelligence community is already taking this approach.
Partner with NSA's AI Security Center. Don't build everything yourself. The AI Security Center exists to develop and integrate AI capabilities across national security systems and to detect and counter AI vulnerabilities. Leverage this expertise.

Strategic Initiatives:

Build intelligence-specific AI models. Generic commercial models won't be optimal for intelligence analysis. Invest in fine-tuning models on intelligence data and workflows. The CIA's focus on AI across operations, including open-source enterprises, provides a model.
Establish cross-intelligence-community AI infrastructure. The current fragmentation---where different agencies build separate systems---is inefficient and creates interoperability challenges. Create shared infrastructure while maintaining appropriate compartmentalization for different clearance levels and access requirements.
Develop AI-enhanced tradecraft. AI isn't just a tool---it changes how intelligence work gets done. Invest in training analysts to work effectively with AI, understanding both capabilities and limitations. Create new analytical methods that leverage AI strengths while mitigating risks like hallucinations or bias.

For Defense and Military Leaders

Immediate Actions:

Assess AI readiness of critical weapons systems. Map current AI integration across command and control, intelligence-surveillance-reconnaissance (ISR), autonomous systems, and decision support. Identify systems that depend on foreign infrastructure or unclassified networks.
Join Pentagon AI initiatives. If you're part of U.S. defense, engage with CDAO's AI Rapid Capabilities Cell, Joint Warfighting Cloud Capability, and Global Information Dominance Experiments (GIDE). These programs exist to accelerate AI adoption---use them.
Test AI in exercises and wargames. Don't wait for perfect AI systems to begin integration. Run AI-enabled exercises now to understand how it changes decision-making, identify organizational barriers, and build operational expertise.

Strategic Initiatives:

Integrate AI into combined joint all-domain operations. The Pentagon's CJADC2 minimum viable capability is operational. Focus on integrating AI into multi-domain operations where decision speed provides military advantage.
Develop AI doctrine and rules of engagement. AI changes the speed and nature of military decision-making. Establish clear doctrine for when humans must be in the loop, what decisions can be AI-assisted, and how to maintain accountability.
Build allied AI interoperability. Future conflicts will involve allied operations with shared information and coordinated action. Ensure your AI systems can exchange data and coordinate decisions with allied forces. NATO's architecture provides a starting framework.

For Allied and Partner Nations

For nations building sovereign AI capabilities:

Be realistic about what sovereignty requires. Complete self-sufficiency is impossible. Focus on mission-critical systems---intelligence, defense, critical infrastructure protection---rather than trying to replicate the entire commercial AI ecosystem domestically.
Leverage allied partnerships. Small and medium nations cannot match U.S. or Chinese AI investments. But allied coalitions can pool resources. France and Germany's collaboration, European defense programs like FCAS and Tempest, and NATO infrastructure demonstrate the model.
Invest in areas of comparative advantage. You don't need to compete across the entire AI stack. France is investing in classified supercomputing. Germany's Helsing focuses on defense applications. Find niches where domestic investment can create genuine capability.
Negotiate carefully with U.S. and Chinese vendors. Sovereignty-as-a-service offerings can provide capability if structured correctly. Insist on air-gapped deployment, domestic data residency with national control of encryption keys, and technology transfer that builds indigenous expertise.
Establish clear red lines. Define explicitly what AI applications are too sensitive for foreign dependencies. Intelligence analysis of sources and methods? Critical weapons systems? Personal data of citizens? Create tiered classifications and match them to sovereignty requirements.

Conclusion: The Sovereign AI Imperative

In October 2024, when President Biden issued the first-ever National Security Memorandum on Artificial Intelligence, he directed agencies across the government to address AI's implications for national security. The classified annex to that memorandum remains secret---but its existence speaks volumes. AI has moved from a promising technology to a national security imperative requiring presidential attention.

The strategic landscape is clear. Nations that build sovereign AI capabilities---controlling their data, infrastructure, and expertise---will maintain the ability to chart independent courses in an AI-enabled world. They will protect their intelligence sources and methods. They will ensure their militaries can make decisions at machine speed without foreign dependencies. They will safeguard their citizens' data and critical infrastructure.

Nations that cede AI sovereignty to foreign powers will become digital dependencies. Their intelligence agencies will risk exposure of sources and methods. Their militaries will face potential kill-switches during crises. Their strategic autonomy will erode until their "choices" merely ratify decisions made elsewhere.

The cost of building sovereign AI capabilities is substantial---measured in billions of dollars, years of effort, and sustained political commitment. But the cost of failing to build these capabilities is strategic obsolescence.

The encouraging reality is that this challenge is solvable. The United States has deployed GPT-4 on classified networks. NATO has established air-gapped AI infrastructure. France is building Europe's most powerful classified supercomputer. Germany's Helsing and France's Mistral AI are developing defense-focused AI. The technology exists. The partnerships are forming. The question is whether your nation is moving fast enough.

For intelligence professionals, defense leaders, and national security officials reading this: you already understand that AI will transform your work as profoundly as the internet did a generation ago. The only question is whether you'll build that AI future on infrastructure you control---or on foundations owned by foreign powers.

The window for establishing sovereign AI is open. But it won't remain open indefinitely. As AI systems become more deeply embedded in national security operations, the switching costs will rise. Dependencies will harden. Strategic options will narrow.

This is the moment to act. Not to study the problem further. Not to wait for perfect solutions. But to begin building the sovereign AI capabilities that will ensure your nation's security and strategic independence for decades to come.

The infrastructure is available. The vendors are engaged. The allied frameworks exist. What's needed now is leadership that recognizes AI sovereignty isn't optional---it's existential.

Key Takeaways

Five critical vulnerabilities make foreign AI dependence a national security risk: supply chain infiltration, data sovereignty violations, geopolitical coercion, training data poisoning, and talent drain.
Air-gapped AI systems are operationally proven: Microsoft's GPT-4 deployment on Azure Government Top Secret, NATO's partnership with Google Distributed Cloud, and the Pentagon's $800 million in AI contracts demonstrate that frontier AI can run on classified networks.
Real-world applications are already delivering results: The NSA uses AI to identify hackers targeting critical infrastructure. CBP has processed 193 million travelers with AI facial recognition, catching 1,500+ individuals using false identities. Intelligence agencies are integrating AI across operations on classified cloud infrastructure.
Building sovereign AI requires a three-phase roadmap: Phase 1 (0-18 months) establishes secure compute, air-gapped networks, and data sovereignty. Phase 2 (12-36 months) develops talent pipelines, model capabilities, and procurement reforms. Phase 3 (24-60 months) integrates operations and builds allied collaboration.
Geopolitical implications are reshaping global power: The U.S.-China AI competition is forcing nations to choose technology alliances. Europe is investing €200+ billion to escape "cybercolony" status. Sovereignty-as-a-service offerings provide capabilities but create subtle dependencies that require careful evaluation.

About the Authors

The Adverant Research Team specializes in analyzing the intersection of artificial intelligence, national security, and geopolitical strategy. This analysis draws on publicly documented government programs, official reports, and academic research.

Sources and References

Keywords

National SecurityGovernment AIDefense TechnologyAir-Gapped SystemsSovereign AI