AI Supply Chain
The AI Supply Chain, in the context of cybersecurity, refers to the entire sequence of components, processes, data flows, and third-party dependencies required to design, develop, deploy, and operate an Artificial Intelligence (AI) system. It is the complex lineage of an AI product, encompassing everything from the raw materials (data) to the final deployed service (the model endpoint or agent).
Understanding this chain is critical because a security failure at any single point can compromise the integrity, confidentiality, or availability of the final AI application.
The AI Supply Chain can be broken down into several primary stages, each introducing unique cyber risks:
Data Acquisition and Preparation: This is the initial stage, involving the collection, cleaning, and labeling of training data. Risks here include data poisoning (introducing malicious or biased data) and data leakage (exposing sensitive information during transport or storage).
Model Development and Training: This phase involves selecting the architecture, writing the code, using development tools, and running the training process on compute infrastructure. Risks include vulnerabilities in third-party libraries, exposed intellectual property (such as proprietary model weights or source code), and compromised build environments.
Third-Party Components: The vast majority of AI systems rely on external dependencies, including pre-trained foundation models (LLMs), open-source software libraries (like TensorFlow or PyTorch), MLOps platforms, and cloud services. A vulnerability or compromise in any of these vendor-supplied components immediately infiltrates the dependent AI application.
Model Deployment and Operation (Inference): This is where the trained model is packaged, deployed to a production environment (on-premise, cloud, or edge), and begins serving predictions. Risks include insecure API endpoints, misconfigured containers, insufficient runtime monitoring, and exposure to live adversarial attacks such as prompt injection.
The overall security posture of the final AI product is only as strong as the weakest link in this multi-stage supply chain.
ThreatNG is an all-in-one external attack surface management, digital risk protection, and security ratings solution. It provides crucial external visibility to secure the complex AI Supply Chain by proactively identifying unauthenticated exposures and dependencies associated with AI vendors, data, and infrastructure.
External Discovery and Inventory
ThreatNG’s ability to perform purely external, unauthenticated discovery without connectors is the foundation for tracking the AI Supply Chain. It acts as an attacker to map out every internet-facing component used by your organization and its third-party vendors.
Technology Stack Identification: This capability is vital for mapping third-party dependencies. ThreatNG uncovers the full stack across nearly 4,000 technologies, detailing specific vendors used, including those in the Artificial Intelligence category (265 technologies), as well as AI Model & Platform Providers and AI Development & MLOps tools. This indicates whether a component of your AI supply chain, such as a specific MLOps tool, is exposed externally.
Domain and Vendor Enumeration: The Domain Name Record Analysis in Domain Intelligence enables the identification of vendors and technologies. ThreatNG’s dedicated Supply Chain & Third-Party Exposure Security Rating is based in part on the enumeration of vendors within Domain Records, SaaS Identification of vendors identified within Cloud and SaaS Exposure, and identification of technologies.
Example of ThreatNG Helping: ThreatNG's Technology Stack module discovers that a critical software vendor used in your AI training pipeline is running an exposed web service using an outdated technology. This immediate, unauthenticated discovery identifies a weak link in your AI supply chain that could be exploited to compromise your systems.
External Assessment for Supply Chain Risk
ThreatNG assesses the exposure risk of third-party components without requiring internal audit access, providing an objective view of supply chain integrity.
Supply Chain & Third-Party Exposure Rating: This dedicated security rating quantifies the risk posed by external parties. It is based on findings across Cloud Exposure (externally identified cloud environments and exposed open cloud buckets), SaaS Identification, Domain Name Record Analysis (vendor enumeration), and Technology Stack Analysis.
Data Leak Susceptibility: This rating is derived from uncovering external digital risks across Cloud Exposure (specifically exposed open cloud buckets). Vendors often use misconfigured cloud buckets to store data temporarily, and if exposed, they can leak proprietary AI training data or model weights from the supply chain.
Web Application Hijack Susceptibility: ThreatNG assesses this by analyzing the presence or absence of key security headers on subdomains. Since many supply chain attacks leverage compromised vendor web applications, this assessment helps identify potential entry points that could lead to lateral movement into your AI environment.
Example of ThreatNG Helping: ThreatNG flags a high Data Leak Susceptibility rating for a third-party data provider due to an exposed open cloud bucket. This immediately alerts your team to a data integrity risk in your AI supply chain, enabling you to enforce stricter data-handling requirements.
Reporting and Continuous Monitoring
ThreatNG provides a proactive, ongoing view of your supply chain risk.
Continuous Monitoring: ThreatNG continuously monitors the external attack surface, digital risk, and security ratings of all organizations, including third-party vendors. This ensures that any new vulnerability or change in a vendor's posture is flagged in real time.
Reporting: ThreatNG provides Security Ratings (A through F) and External GRC Assessment mappings. This allows security leaders to track progress on addressing vulnerabilities with vendors and measure the effectiveness of collaborative security initiatives.
Investigation Modules
ThreatNG's Investigation Modules allow for targeted reconnaissance to validate supply chain weaknesses.
Dark Web Presence (DarCache Dark Web): This module monitors organizational mentions of related people, places, or things, and associated Ransomware Events. This allows for proactive mitigation of risks, as ransomware groups often target weak supply chain links.
Sensitive Code Exposure: This module discovers public code repositories and specifically looks for Access Credentials and Configuration Files. If a vendor accidentally pushes a key for an AI platform to a public repository, ThreatNG immediately discovers the leaked secret, which is a direct supply chain compromise.
Domain Name Permutations: This module detects manipulations and additions of a domain, including homoglyphs and TLD-swaps. This helps identify potential phishing or brand impersonation attempts targeting your supply chain partners or customers.
Example of ThreatNG Helping: The Dark Web Presence module identifies mentions of a key AI platform vendor associated with a recent Ransomware Event tracked in DarCache Ransomware. This intelligence provides advanced warning that a critical component in your AI supply chain is under active threat, allowing you to take preemptive measures.
Complementary Solutions
ThreatNG's external intelligence on vendors provides crucial, objective data to internal risk management processes.
Complementary Solutions (Third-Party Risk Management Platforms): ThreatNG's Supply Chain & Third-Party Exposure Security Rating and detailed findings (like exposed ports or outdated technology stacks) can be automatically fed to a Third-Party Risk Management (TPRM) platform. This external, unauthenticated data validates or contradicts vendor-supplied security questionnaires, allowing the TPRM platform to immediately prioritize the riskiest vendors for deeper due diligence, especially those exposing AI-related assets.
Complementary Solutions (GRC Tools): ThreatNG's External GRC Assessment directly maps external vulnerabilities to compliance standards like HIPAA and ISO 27001. If ThreatNG detects an exposed cloud bucket containing unauthenticated data, it provides the finding to the internal GRC tool, which can then automatically generate compliance violation reports related to data protection requirements, thereby strengthening internal AI supply chain governance.
