The healthcare industry’s rapid adoption of artificial intelligence has introduced a new layer of complexity to cybersecurity and operational risk: the hidden dependencies embedded within third-party AI supply chains. As hospitals and health systems integrate AI tools for diagnostics, workflow optimization, and patient engagement, they increasingly rely on vendors whose own components may originate from opaque or unverified sources. This creates blind spots where vulnerabilities — ranging from data poisoning to model drift or unauthorized data sharing — can proceed undetected by conventional security assessments.
To address this growing concern, the Health Sector Coordinating Council (HSCC) Cybersecurity Working Group has released a comprehensive 109-page guide titled “Managing Third-Party Artificial Intelligence Risk in Healthcare: A Supply Chain Transparency Framework.” Published in April 2024, the document outlines a seven-phase lifecycle approach designed to help healthcare organizations identify, assess, and mitigate risks associated with external AI components throughout their lifecycle — from procurement to decommissioning.
The guide responds to increasing regulatory scrutiny and real-world incidents involving AI-enabled medical devices and software. In 2023, the U.S. Food and Drug Administration (FDA) issued multiple safety communications highlighting concerns about undisclosed changes to AI algorithms in radiology and cardiology tools, underscoring the need for greater transparency in how these systems are built, updated, and monitored.
Understanding the Hidden Layers of AI Supply Chains
Unlike traditional software, AI systems often depend on a complex web of components: pre-trained models, training datasets, labeling services, cloud infrastructure, and application programming interfaces (APIs), many of which are sourced from third parties. A 2023 study by the National Institutes of Health found that over 60% of AI models used in clinical settings incorporated at least one component from an external vendor not directly managed by the healthcare organization deploying the tool.
These dependencies can introduce risks that standard vendor questionnaires or penetration tests fail to catch. For example, a model trained on biased or non-representative data may produce inaccurate diagnoses for certain demographic groups. Similarly, an update to a cloud-based AI service could alter performance without the healthcare provider’s knowledge, potentially violating regulatory requirements for validated medical devices under FDA’s Software as a Medical Device (SaMD) framework.
The HSCC guide emphasizes that transparency is not merely about knowing who supplies a component, but understanding how that component is developed, maintained, and secured across its entire lifecycle. This includes visibility into data provenance, model versioning, change management practices, and third-party subcontractor relationships.
The Seven-Phase Lifecycle Framework
At the core of the HSCC guidance is a structured, seven-phase lifecycle model adapted from established supply chain risk management practices but tailored to the unique characteristics of AI in healthcare. The phases are:
- Plan and Define Requirements: Organizations establish clear criteria for AI acquisition, including performance expectations, data governance standards, and security controls aligned with HIPAA and NIST frameworks.
- Assess and Select Vendors: Due diligence extends beyond financial stability to include scrutiny of the vendor’s AI development practices, data sources, model validation methods, and subcontractor disclosures.
- Negotiate and Contract: Contracts should include specific clauses requiring transparency about AI components, rights to audit, notification of changes, and liability for harm caused by undisclosed modifications.
- Onboard and Integrate: Before deployment, healthcare systems should verify model performance in local environments, validate data inputs, and implement monitoring for drift or degradation.
- Operate and Monitor: Continuous oversight includes tracking model outputs, logging changes, and establishing alerts for anomalies that may indicate data poisoning, adversarial attacks, or concept drift.
- Maintain and Update: Any update to an AI component — whether a model retrain, infrastructure change, or API modification — must undergo a risk reassessment similar to initial deployment.
- Decommission and Dispose: At end-of-life, organizations must ensure secure deletion of data, models, and associated metadata, particularly when dealing with sensitive patient information used in training or fine-tuning.
Each phase includes recommended actions, documentation requirements, and mapping to existing cybersecurity frameworks such as the NIST Cybersecurity Framework (CSF) and the Health Industry Cybersecurity Practices (HICP). The guide similarly provides sample questionnaires, risk scoring matrices, and contract language templates to support implementation.
Why This Matters for Healthcare Leaders
For chief information officers (CIOs), chief information security officers (CISOs), and clinical engineers, the guide offers a practical pathway to meet both internal risk management obligations and evolving regulatory expectations. In the United States, the FDA’s proposed rule on marketing submissions for cloud-based AI products emphasizes the need for lifecycle transparency, even as the European Union’s AI Act imposes strict obligations on high-risk AI systems, including those used in healthcare.
Failure to manage third-party AI risk can lead to clinical harm, regulatory penalties, reputational damage, and costly recalls. A 2022 incident involving an AI-powered sepsis prediction tool, later found to have degraded performance after an undocumented update, contributed to delayed treatments in several U.S. Hospitals — a case cited in the HSCC guide as a cautionary example of opaque supply chain risks.
Conversely, organizations that proactively implement supply chain transparency report improved confidence in AI performance, faster incident response, and stronger alignment with audit and accreditation standards. Early adopters include major academic medical centers in Germany, Canada, and Japan, where similar guidelines are being adapted to local regulatory contexts.
Implementation Challenges and Support Resources
While the framework provides a clear roadmap, adoption faces practical barriers. Smaller hospitals and rural health systems may lack the staffing or expertise to conduct deep technical evaluations of AI components. To address this, the HSCC recommends leveraging shared services through health information exchanges (HIE), group purchasing organizations (GPO), or regional cybersecurity collaboratives.
The guide also points to emerging tools designed to automate aspects of AI supply chain monitoring, such as software bills of materials (SBOMs) tailored for machine learning models — sometimes referred to as “AI BOMs” or “model cards.” Initiatives like the NIST AI Risk Management Framework and the HL7 FHIR AI specification are working to standardize how AI components are described, tracked, and exchanged across systems.
Training is another critical component. The HSCC encourages healthcare organizations to integrate AI risk management into existing cybersecurity awareness programs and to partner with biomedical engineering teams, data scientists, and clinical stakeholders to ensure that technical controls align with patient safety goals.
What Comes Next
The HSCC Cybersecurity Working Group plans to host a series of webinars and regional workshops throughout 2024 and 2025 to support implementation of the guide. The next public event is a virtual workshop scheduled for September 18, 2024, focusing on practical applications of the seven-phase model in hospital settings.
Healthcare organizations seeking to assess their current AI supply chain practices can begin by inventorying all AI-driven tools in use, identifying third-party dependencies, and reviewing existing vendor contracts for transparency gaps. The HSCC guide is available for free download from the HSCC Cybersecurity Working Group’s resource library.
As AI continues to reshape healthcare delivery, ensuring that these systems are not only effective but also trustworthy and secure will require ongoing collaboration between clinicians, technologists, regulators, and vendors. The HSCC’s framework offers a vital step toward building that trust — one transparent link at a time.
We invite our readers to share their experiences with AI risk management in healthcare. Have you encountered challenges with third-party AI dependencies? What strategies have worked in your organization? Join the conversation in the comments below and help us spread awareness by sharing this article with colleagues in health IT, clinical engineering, and patient safety.