Allies' Military AI Systems Face Interoperability Challenges

The integration of artificial intelligence (AI) systems into militaries around the world has raised concerns about interoperability between different AI systems.

The Center for Strategic and International Studies (CSIS) and Scale AI tested seven major foundation models across 400 diplomatic and security scenarios in 2025. The results showed that despite being presented with identical international crises, the recommendations from the seven models diverged significantly, with some models exhibiting unique national bias.

This has significant implications for military operations, where different AI systems may be used by different allies on the same battlefield. The US, Japan, and South Korea are taking different approaches to integrating AI systems, with the US focusing on strengthening AI-driven interoperability within its Indo-Pacific alliances.

## Japan's Two-Track Strategy

Japan has adopted a calculated two-track strategy, with summer 2026 being the watershed. Before June, 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.

## South Korea's Independent Approach

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.

## The Philippines' Unclear Direction

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.

## The Risk Management Framework (RMF)

The critical link in connecting disparate AI systems into a single, integrated decision-making structure is 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.

## The AI RMF: A Common Language for the Alliance

The AI RMF provides a framework that makes trustworthiness measurable and comparable. It quantitatively reveals how resilient the model is when an adversary attempts to deceive it via camouflage or spoofing. It shows how much identification accuracy degrades in environments outside its training data, such as fog or nighttime, and how reliability fluctuates under poor sensor data quality.

| Model | Accuracy | Resilience | Identification Degradation | | --- | --- | --- | --- | | US System | 99% | 80% | 20% | | Korean System | 95% | 70% | 30% |

This measurement matters not just for technical accuracy, but because it directly relates to whether humans can maintain genuine control over AI. When discussing autonomous systems, the phrase "human-in-the-loop" is frequently used. However, there is a vast difference between a human sitting in that loop as a mere formality and a human who substantively controls the system by understanding the rationale behind its judgments.

## The AI RMF: A Necessary Component of AI Interoperability

The necessity of the AI RMF extends even further. In the current rush to field AI on the battlefield, it is easy to miss the fact that you cannot have maximum performance and maximum security simultaneously. Making an AI highly resilient against enemy deception often blunts its speed and accuracy; conversely, pushing solely for raw performance leaves the system vulnerable to manipulation. Gaining ground on one side requires sacrificing some on the other. The AI RMF quantifies this trade-off by showing exactly where each system stands between security and performance.

The AI RMF transcends individual system verification to become the common language of the alliance. When integrating different AI systems into a single operation, nations can look at the measured data and mutually agree on where to set the baseline of trust for a specific coalition mission. Determining that balance remains a human responsibility, but without these measurements, such an agreement becomes a baseless compromise.