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MakinaRocks が韓国の軍事ネットワークに AI オペレーティング システムを導入

MakinaRocks は、同社の Runway AI オペレーティング システムが、2026 年の乙支フリーダム シールド演習中に韓国の韓国統合指揮統制システム (KJCCS) にインストールおよびテストされ、この国の閉鎖された戦場ネットワークへの初めての配備となったと報告しました。

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Source-provided image accompanying MakinaRocks deploys AI operating system on Korean military network
出典参照記録されたソース
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en.sedaily.com
ソースリンク
en.sedaily.comhttps://en.sedaily.com/technology/2026/09/28/makinarocks-deploys-first-ai-operating-system-on-korean
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重要な用語

人工知能 (AI)
パターン認識、推論、言語、意思決定を必要とするタスクを実行するシステムを構築する広範な分野。
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テキスト、画像、オーディオ、ビデオ、コードなどの新しいコンテンツを生成する AI システム。
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何が起こったのか

MakinaRocks stated that it installed and tested its Runway AI operating system on the Korea Joint Command and Control System (KJCCS), a core command and control system for joint operations in South Korea. The deployment was demonstrated during the 2026 Ulchi Freedom Shield (UFS) exercise. The system enables -based information and allows AI models to run within the closed, high-security battlefield network. Military authorities developed an AI service called JEDI-J to operate within this environment, which was verified in a military operating environment during the exercise.

MakinaRocks announced on September 28 that it had installed and tested its Runway AI operating system on South Korea's Korea Joint Command and Control System (KJCCS). This marks the first deployment of an AI operating system on the country's battlefield network, which is a closed system subject to tighter security controls than general defense administrative networks.

The company participated in a project to demonstrate -based information and operating technology for KJCCS. MakinaRocks supplied the Runway AI operating system, which allows AI models and services to run inside the KJCCS environment. The company also developed an AI function specifically for searching data held within the system.

Military authorities developed an AI service named JEDI-J to run within the Runway environment. The AI infrastructure and services were verified in a military operating environment during the 2026 Ulchi Freedom Shield (UFS) exercise, which took place last month. During the exercise, the military used JEDI-J and other AI functions in operations, confirming that can be put to practical use even on a closed network.

The Runway-based AI operating environment is designed to be installed and tailored for a distributed AI setup. This setup can link data centers, mobile servers, and edge devices deployed in the field, addressing the specific logistical and security challenges of battlefield operations.

ソースの詳細: en.sedaily.com ↗

なぜそれが重要なのか

This deployment represents a significant milestone in integrating artificial intelligence into high-security, closed military networks, which have traditionally been resistant to AI adoption due to strict security controls. By successfully running on the KJCCS, the project demonstrates that practical AI applications, such as data and operational support, can function in isolated environments. This could accelerate the adoption of AI in defense sectors globally, where data sovereignty and security are paramount, potentially altering how military command and control systems process information and support decision-making in the field.

The deployment addresses a critical gap in military technology: the ability to utilize advanced AI tools in high-security, isolated environments. Traditionally, the strict security protocols of battlefield networks have prevented the integration of cloud-based or standard commercial AI solutions.

By verifying the system during the 2026 UFS exercise, the project provides concrete evidence that can function effectively in real-world military operations. This validation is crucial for gaining trust from military leadership and for scaling similar projects to other defense systems.

The use of a distributed AI setup linking data centers, mobile servers, and edge devices suggests a move toward resilient, on-premise AI infrastructure. This approach is likely to become a standard requirement for defense AI, as it ensures data sovereignty and operational continuity even when external connectivity is unavailable or compromised.

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System Requirements:
Best ArchitecturePure RAGRecommended pattern
Hallucination RiskVery LowGrounding efficacy
Update Cost$0 (Vector sync)Ongoing maintenance
Core takeaway: Fine-tuning teaches models how to speak (form, style, syntax); RAG teaches models what to say (verifiable facts). Never use fine-tuning alone for factual memory.
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次に見るべきもの

Monitor for further expansions of the Runway platform to other defense networks or allied military systems. Watch for details on the specific capabilities of the JEDI-J AI service and whether it is being integrated into broader operational workflows. Additionally, observe if other defense contractors or AI firms announce similar deployments on closed military networks, which could signal a broader industry shift toward secure, on-premise AI infrastructure for national security applications.

Look for announcements regarding the expansion of the Runway platform to other branches of the South Korean military or to allied nations' defense networks. The success of this initial deployment may serve as a proof of concept for broader international adoption.

Monitor for further details on the JEDI-J AI service, including its specific capabilities, data sources, and how it integrates with existing command and control workflows. Understanding the practical applications of JEDI-J will provide insight into the operational impact of AI in military decision-making.

Observe the response from other defense contractors and AI companies. If MakinaRocks' deployment is seen as a significant competitive advantage, it may prompt rival firms to accelerate their own efforts to develop secure, on-premise AI solutions for military use.

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