Introduction
The 2025 State of Cybersecurity Report by Check Point reveals a dramatic rise in attacks on technology supply chains. Software, hardware, and semiconductor companies are now prime targets, with the hardware and semiconductor sectors experiencing a staggering 179% increase in weekly cyberattacks. Total attacks now exceed 1,400 weekly incidents. Cybercriminals exploit these vulnerabilities for financial gain, espionage, or operational disruption—placing the AI supply chain squarely in the crosshairs.
Understanding AI Supply Chain Risks
AI supply chain risks encompass cybersecurity, operational, and ethical vulnerabilities at every stage of an AI system’s lifecycle—development, sourcing, training, deployment, and maintenance. The complexity of AI systems arises from their reliance on numerous interconnected components such as:
- Software libraries
- Datasets
- Cloud services
- Hardware
- Third-party models and APIs
Each of these presents unique vulnerabilities, summarized below:
| Area | Potential Risk | Impact |
| Third-Party Components | Hidden backdoors in open-source libraries | Unauthorized access, data leakage |
| Training Data | Poisoned datasets | Model corruption, loss of integrity |
| Model Manipulation | Downloading tampered models | Compromised behavior, data breaches |
| Vendor Dependency | Relying on insecure cloud vendors | System-wide exposure if vendor is breached |
| Hardware Risks | Malicious firmware in GPUs/TPUs | Attacks that bypass software-level defenses |
| Compliance Risks | Non-adherence to standards (e.g., GDPR, NIST, ISO) | Legal liabilities, reputational damage |
| Updates & Patches | Trusting automatic updates without verification | New post-deployment vulnerabilities |
Key AI Supply Chain Risks from Third-Party Models & APIs
Third-party AI models and APIs introduce substantial vulnerabilities. CIOs must proactively mitigate these risks through governance, validation, and monitoring.
| Risk Category | Risk Description | CIO Concerns |
| Model Tampering | Backdoored or poisoned pre-trained models | Compromised outputs, regulatory breaches, loss of trust |
| Unvetted APIs | Weak authentication, poor vendor stability, or data leaks | Data exfiltration, service disruption, system compromise |
| Lack of Transparency | Opaque model training sources or limitations | Hidden bias, poor performance, compliance violations |
| Licensing/Legal Risk | Improper licensing or unauthorized data usage | Intellectual property issues, legal exposure |
| Data Residency/Sovereignty | Cross-border data flow via third-party APIs | Violates GDPR, HIPAA, or local data laws |
| Dependency Risk | Over-reliance on third-party vendors | Loss of control, vendor failure, operational risk |
CIO Action Plan: Securing the AI Supply Chain
Implement a Governance Framework
- Adopt frameworks like NIST AI RMF, ISO 42001, or NIST SP 800-161r1.
- Establish formal intake, vetting, validation, and review workflows for third-party AI components.
Strengthen Supply Chain Integration
- Perform Software Composition Analysis (SCA) on all AI/ML elements.
- Require Software Bills of Materials (SBOMs) to map and track dependencies.
- Enforce cryptographic hash validation for all AI models.
Secure and Monitor APIs
- Enforce TLS 1.2+ and OAuth2 protocols.
- Implement Zero Trust Architecture: enforce least privilege and verify all API interactions.
- Enable audit logs and access monitoring.
Ensure Legal and Regulatory Compliance
- Mandate contracts that define responsibilities for data handling, IP rights, and security.
- Verify vendor compliance with GDPR, the EU AI Act, and local regulations via DPAs (Data Processing Agreements).
Conduct Red Teaming & Adversarial Testing
- Simulate attacks (e.g., model inversion, prompt injection) to expose vulnerabilities.
- Use tools like IBM ART and Microsoft Counterfit for adversarial robustness testing.
Dashboard KPIs for AI Supply Chain Risk Management
| KPI | Description |
| SBOM Coverage (%) | Percentage of models with full dependency traceability |
| API Risk Rating | Score based on automated and manual API risk assessments |
| Model Integrity Checks | Frequency and outcomes of model validation and hash verification |
| Compliance Readiness Score | Alignment with GDPR, ISO 42001, and NIST frameworks |
Conclusion
AI is redefining the global supply chain landscape, promising efficiency and innovation. However, this transformation also multiplies risk. CIOs must adopt a proactive, multi-layered security strategy to ensure that the integration of third-party models and APIs strengthens—rather than weakens—their enterprise. By embracing robust governance, technical safeguards, and ongoing vigilance, organizations can build secure and resilient AI supply chains.
