Asia Defense | Security | East Asia

Are Allies’ Military AI Systems Interoperable? 

The U.S., South Korea, and Japan are pursuing different AI-driven combat systems. That will lead to different recommendations on the same battlefield.

Soldiers assigned to the 304th Expeditionary Signal Battalion-Enhanced install and inspect communications equipment in support of Ulchi Freedom Shield at CP Tango in South Korea, Aug. 11, 2026.

How can allies using different AI systems on the same battlefield trust one another? While current discussions about military artificial intelligence (AI) focus on simply fielding these systems, allied militaries soon have to address this exact question. 

In 2025, the Center for Strategic and International Studies (CSIS) and Scale AI tested seven major foundation models across 400 diplomatic and security scenarios. Despite being presented with identical international crises, the recommendations from the seven models diverged significantly. Some were uniquely hawkish, and every model exhibited some degree of national bias. This demonstrates that AI is by no means a homogenous tool.

Now these fractured algorithms are being integrated into many militaries on earth. We may be living through one of the final eras where an AI-equipped military might face an opponent operating without it. Before long, most global alliances will be thoroughly armed with AI – but each will be operating with a radically different version. Consequently, the real question is no longer who possesses AI, but rather, whether allies can trust different AI systems on the same battlefield. 

Nations are already scrambling to secure interoperability between their chosen AI frameworks and those of their allies. The United States is rapidly strengthening AI-driven interoperability within its Indo-Pacific alliances. For instance, during the September 2025 Freedom Edge trilateral exercise between the United States, South Korea, and Japan, the nations demonstrated real-time data exchange by linking their respective simulation systems using AI tools.

The scope of these discussions is vast, ranging from real-time sensor integration and instant target identification to improving allied AI literacy. On the surface, this sounds like traditional alliance cooperation rhetoric, but beneath it lies cutting-edge military technology poised to reshape how coalition warfare operates.

The problem lies in what comes next. If every ally adopted Palantir’s Maven Smart System, for instance, integration would be relatively straightforward. However, with the rise of “AI sovereignty,” nations are parting ways on which AI-driven combat systems to adopt.

Japan has adopted a calculated two-track strategy, with summer 2026 being the watershed. Before June, under the Japan-U.S. alliance framework, Tokyo actively leveraged existing U.S. systems when necessary, while fostering domestic companies like SoftBank and Sakura Internet as the pillars of its sovereign AI infrastructure. Microsoft and Open AI’s recent investment signings with the aforesaid Japanese tech giants, while on a commercial basis, definitely seemed to be a signal that Tokyo would not rule out U.S. AI capabilities taking root in Japanese soil. 

Perhaps the adoptions of Palantir-based solutions by Japanese firms, namely Fujitsu, was a harbinger of a bold change. As of mid-August, Japan is considering Palantir’s Maven Smart System as well as Anduril’s Lattice. The government, according to Nikkei, “envisions bringing in multiple AI systems” for the best operational decision-making, while using different systems for specific, tailored applications. This is best interpreted as a dual strategy that leverages U.S. technology while cultivating domestic industry to safeguard sensitive Self-Defense Force information and intelligence.

South Korea, by contrast, leans toward building systems based on independent technology rather than adopting U.S. platforms. With the government declaring 2026 as the year of the Defense AI Transformation, Naver recently launched a dedicated defense AI organization, entering the military market with its own foundation models and sovereign AI capabilities. Private-sector competition is heating up as SK Telecom and Hanwha Systems join the fray. 

Meanwhile, the Philippines has yet to chart a clear direction. Lacking large domestic AI enterprises, it may quickly and boldly adopt foreign platforms like Palantir in the future.

Like any multilateral security initiative, this plan to connect disparate AI systems into a single, integrated decision-making structure demands a core requirement that must be met. Deploying and operating AI is one thing; accurately understanding the capabilities and limitations of an ally’s system is an entirely different matter.

The critical link is highly likely to be a concept that, despite its immense importance, has flown under the radar: the Risk Management Framework (RMF). Verification is required at two distinct levels. Traditional RMFs handle the security accreditation and interoperability standards of the middleware connecting different platforms. However, the newer, less familiar dimension of AI model trustworthiness is handled by an AI-specific layer within that framework – specifically, the National Institute of Standards and Technology (NIST) AI RMF. Though largely invisible, the AI RMF could become the true unsung hero that binds U.S.-made systems and indigenous allied AIs into a single, integrated network.

The aforementioned CSIS and Scale AI experiment highlights the issue: If models diverge this drastically in judgment,........

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