Japan: AI and Machine Learning Supply Chain Risks: Japan Signs on to International Guidance

Japan’s National Cybersecurity Office (NCO, formerly NISC) has joined the United States, the United Kingdom, Australia, Canada, and other partner agencies in endorsing international guidance on supply chain risks in artificial intelligence (AI) and machine learning.

This move signals that AI security is no longer viewed as a narrow technical issue for individual vendors or governments, but as a shared international challenge requiring coordinated standards and oversight.

The guidance highlights that AI systems rely on a far more complex supply chain than conventional software. They are built not only on code and infrastructure, but also on data, pre-trained models, training environments, and third-party services. As organisations increasingly adopt AI to improve efficiency and decision-making, their exposure to vulnerabilities across this chain grows, raising the risk that attackers could compromise the confidentiality, integrity, or availability of critical systems.

Managing Risk Across the AI Lifecycle

A central message of the guidance is that AI supply chain risk must be managed across the full product lifecycle, rather than treated as a one-off procurement issue.

To achieve this, organisations should:

• identify all relevant suppliers and components
• increase transparency through tools such as software bills of materials (SBOMs) and AI bills of materials (AIBOMs)
• update governance frameworks to address AI-specific attack surfaces

The guidance emphasises the importance of:

• continuous risk assessment and threat modelling
• vulnerability mapping
• dedicated incident response planning

It also stresses the need for robust vendor due diligence, early clarification of responsibilities under shared-responsibility models, and careful review of how vendors access, use, store, and transfer organisational data.

Internal preparedness is equally important. Staff involved in supply chain management should receive AI-focused security training, and organisations should establish clear communication channels for reporting and responding to emerging threats.

Key Risk Areas and Mitigation Measures

The guidance identifies five core areas of risk: data, machine learning models, AI systems, infrastructure and hardware, and third parties.

Data Risks

Risks include:

• low-quality or biased datasets
• data poisoning
• leakage of sensitive training data

Mitigation measures include:

• quarantining external data
• input sanitisation
• tracking data provenance
• benchmarking across datasets and models
• applying privacy-preserving techniques

Model Risks

Risks include:

• malicious code embedded during serialization
• model poisoning
• malware hidden in weights or metadata

Mitigation measures include:

• using safer file formats
• sourcing from trusted providers
• reproducible builds
• adversarial testing and training
• pruning and ongoing monitoring for drift or abnormal behaviour

Systems and Infrastructure Risks

Recommended controls include:

• integrity checks and digital signatures
• secure deployment based on least-privilege principles
• signed drivers and verified boot
• network segmentation

Third-Party Risks

The guidance calls for:

• rigorous vendor assessment and continuous monitoring
• contractual safeguards limiting data use
• clarity on data storage locations
• preservation of audit rights

Bottom Line

The guidance positions supply chain transparency as a foundational requirement for trustworthy AI governance.

For organisations deploying AI, the message is clear: managing risk is not limited to internal systems. It requires end-to-end visibility, stronger vendor controls, and continuous oversight across an increasingly complex and interconnected supply chain.

By Anderson Mori Tomotsune, Japan, a Transatlantic Law International Affiliated Firm. 

For further information or for any assistance please contact japan@transatlanticlaw.com

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