An AI Bill of Materials in healthcare gives organizations a clearer view of what actually sits behind the artificial intelligence systems entering clinical, administrative, and operational workflows. Healthcare organizations are rapidly adopting AI for documentation, scheduling, billing, imaging, patient communication, clinical decision support, fraud detection, and analytics. Yet the convenient interface employees see may represent only a small portion of the technology and data supporting the application.

An AI system is rarely a single, self-contained product. Behind it may be a foundation model, training and evaluation datasets, open-source software, cloud infrastructure, application programming interfaces (APIs), retrieval databases, plugins, service accounts, and automated workflows. Any change or weakness within those components could potentially affect the system’s security, privacy, reliability, or compliance posture.

Before allowing an AI system to process Protected Health Information (PHI) or participate in important healthcare workflows, organizations need to understand what they are actually deploying. That means knowing where the technology came from, what data it can reach, what external services support it, and who is responsible for maintaining it. An AI Bill of Materials, commonly shortened to AIBOM, can help establish that visibility.

An AI Model Is Only One Component of the System

When evaluating healthcare AI, it is easy to focus on the model itself. Organizations may compare accuracy, capabilities, speed, cost, and clinical or administrative usefulness before approving a product. Cybersecurity teams, however, need to examine the larger ecosystem supporting those features.

A seemingly simple AI assistant used to summarize healthcare policies might depend on several interconnected technologies. It could retrieve documents from cloud storage, transmit prompts to an external model provider, authenticate through an identity platform, depend on open-source packages, and preserve conversations in application logs. Each dependency becomes another component that must be understood and appropriately protected.

This is why healthcare AI governance cannot stop at documenting the model name. Security teams need visibility into models and versions, data sources, software dependencies, APIs, permissions, vendors, hosting environments, and monitoring responsibilities. Without that information, organizations can unknowingly introduce dependencies they are poorly prepared to secure or investigate.

What Is an AI Bill of Materials?

An AI Bill of Materials is an emerging approach to documenting the components, dependencies, and lineage associated with an artificial intelligence system. The concept shares similarities with a Software Bill of Materials (SBOM), which provides visibility into software components and dependencies. An AIBOM expands that concept to address characteristics specific to AI systems.

There is not yet a single universally mandated AIBOM format that every healthcare organization must follow. Organizations can nevertheless build practical AI inventories that capture the information necessary for cybersecurity, privacy, compliance, procurement, and incident response. The objective is not documentation for its own sake, but making AI understandable and governable throughout its lifecycle.

A practical AI Bill of Materials in healthcare may include:

• Model name, provider, version, and architecture
• Training, tuning, evaluation, and retrieval data sources
• Data ownership, provenance, and licensing
• Software libraries and package versions
• APIs, plugins, agents, and connected tools
• Cloud infrastructure and hosting environments
• Security and privacy testing results
• Known vulnerabilities and limitations
• User groups and access permissions
• Update, retraining, and modification history
• Responsible internal owner
• Incident response and rollback procedures

NIST’s AI Risk Management Framework resources encourage organizations to maintain inventories and documentation that support AI governance and risk management. For healthcare organizations, this inventory can become a central reference point connecting technical components with ownership, security responsibilities, data access, and operational dependencies.

Why AI Inventory Matters for Healthcare Cybersecurity

Healthcare organizations cannot effectively secure assets they do not understand. This principle already applies to endpoints, servers, medical devices, cloud resources, and internet-facing infrastructure. Artificial intelligence introduces another category of technology that needs similarly disciplined inventory management.

An AI inventory allows security teams to answer practical questions before a problem becomes an emergency. They can identify which systems process PHI, which applications rely on external model providers, and which AI services depend on particular software components. That visibility becomes especially important when vulnerabilities or vendor incidents are discovered.

Without an inventory, incident response can quickly turn into an organization-wide investigation. Teams may need to search contracts, repositories, cloud consoles, tickets, and vendor communications simply to determine whether a vulnerable component exists. An AIBOM can substantially shorten that discovery process.

Model Lineage Provides Critical Context

Model lineage describes where an AI model came from and how it evolved before and after deployment. Healthcare organizations should understand the model provider, version, architecture where relevant, customization history, and significant updates affecting the system. This information provides context when behavior or security characteristics change.

For example, a vendor may replace an underlying foundation model without dramatically changing the user interface. That change could affect outputs, privacy characteristics, integrations, or security assumptions established during the organization’s original assessment. Without model lineage, healthcare teams may not realize that the system they approved is technically different from the system currently operating.

Maintaining model versions also supports investigations when unexpected results occur. Security and governance teams should be able to determine which version generated an output at a particular point in time. Traceability becomes increasingly important as AI systems influence higher-impact healthcare processes.

Data Lineage Matters Just as Much

AI behavior depends heavily on the information used to train, fine-tune, evaluate, or provide context to the system. Healthcare organizations therefore need visibility into the origin and transformation of relevant datasets. Unknown or poorly documented data can introduce privacy, security, licensing, reliability, and governance concerns.

Data lineage should document details such as:

• Original data source
• Collection method
• Authorized users
• Cleaning and transformation processes
• Label modifications
• De-identification procedures
• Dataset versions
• Training or update dates

These considerations become particularly important when systems rely on medical records, third-party datasets, internal documents, or retrieval-augmented generation. If an AI system produces an unexpected or unsafe response, investigators should be able to trace the relevant model, data, configuration, and software involved. Without lineage, troubleshooting begins with assumptions rather than evidence.

A Healthcare AIBOM Scenario

Consider a medical billing company that deploys an AI assistant to analyze claims documentation. The application uses a commercial foundation model, several open-source libraries, an internal document repository, cloud infrastructure, and an API connection to the organization’s billing platform. From an employee’s perspective, all of these components appear as a single application.

Several months later, a serious security vulnerability is disclosed in one of the supporting software libraries. The organization now needs to determine whether it uses the affected package, which version is deployed, and which AI workflows depend on it. It must also understand whether the vulnerable component can interact with PHI or other sensitive systems.

Without an accurate AI inventory, answering these questions may require an urgent investigation across multiple departments and vendors. With a maintained AIBOM, security teams can identify affected systems more quickly, evaluate exposure, prioritize remediation, and document their response. The value of component visibility becomes especially clear when something goes wrong.

AI Systems Do Not Remain Static

An AI application approved today may operate differently several months from now. Model providers release new versions, developers replace software libraries, administrators expand permissions, and teams connect additional data sources. Plugins and agents may also give an AI system entirely new capabilities.

A tool originally approved for summarization might eventually gain the ability to search internal records, send messages, create tickets, or interact with external applications. Those changes alter what the AI can access and what could happen if the system is misused or compromised. Governance therefore needs to account for changes throughout the AI lifecycle.

An AI Bill of Materials in healthcare should consequently be treated as a living record. Material changes to models, permissions, integrations, hosting, data sources, and software dependencies should trigger appropriate review. An inventory that is created during procurement and never updated quickly loses much of its security value.

Start by Inventorying Every Approved AI System

Healthcare organizations should maintain a centralized inventory of AI technologies operating throughout their environments. This should include obvious standalone AI products as well as AI features embedded within existing EHRs, medical devices, cloud platforms, cybersecurity tools, and administrative software. Internally developed applications and employee-facing assistants should also be included.

Shadow AI makes this process more challenging because employees may adopt tools without formal approval. Organizations therefore need both governance and technical discovery processes capable of identifying AI use beyond the official technology catalog. The goal should be a realistic inventory rather than a list containing only what leadership already knows about.

Every inventory entry should identify its business purpose and responsible owner. Without ownership, security findings, vendor notifications, and required updates may have nowhere to go.

Document Exactly What Data the AI Can Access

Healthcare AI inventories should clearly identify the categories of information available to each system. An application might process PHI, claims information, workforce data, internal policies, credentials, financial records, or proprietary business information. Understanding this access helps organizations evaluate the consequences of misuse or compromise.

Security teams should also distinguish between data submitted directly through prompts and information retrieved automatically from connected systems. An employee may enter very little information manually while an AI agent independently accesses databases or cloud repositories. Both pathways belong in the organization’s risk analysis.

Data access should be reviewed whenever integrations or permissions change. An AI tool that initially handles public information may eventually become capable of accessing highly sensitive healthcare records.

Track Software, Models, APIs, and Vendors

Dependencies are a central component of an effective AIBOM. Organizations should record the foundation models, software libraries, cloud platforms, APIs, plugins, retrieval systems, and third-party services that support each AI application. Version information should be preserved wherever practical.

This dependency map creates an AI supply-chain view that security teams can use when new vulnerabilities or incidents emerge. If a model provider, open-source library, or cloud service experiences a security problem, the organization can quickly identify affected workflows. This makes vulnerability response substantially more targeted.

Vendor relationships deserve particular attention because healthcare organizations may not directly control every component. Procurement and security teams should require enough transparency to understand critical dependencies and meaningful changes.

Monitor AI Dependencies for Vulnerabilities

Maintaining an inventory becomes particularly valuable when paired with continuous vulnerability monitoring. Newly disclosed weaknesses in software packages, model-serving platforms, APIs, cloud services, or authentication components may affect multiple AI systems simultaneously. Security teams need a reliable way to determine organizational exposure.

The same principle already applies to conventional vulnerability management. When a critical software vulnerability becomes public, organizations search their asset inventories to identify affected systems. An AIBOM extends this capability into the AI environment.

Teams should also establish processes for responding to vendor security notifications. Knowing that a vulnerability exists provides limited value if the organization cannot determine where the affected technology is deployed.

Prepare Rollback and Shutdown Procedures

Healthcare organizations should know how to safely disable an AI capability before an emergency occurs. This may require revoking service credentials, disconnecting APIs, disabling plugins, restricting retrieval sources, reverting to a previous model version, or temporarily returning to manual workflows. These procedures should be documented for systems supporting important operations.

A shutdown plan is especially important when AI applications interact directly with sensitive healthcare information or perform automated actions. Incident responders should not have to discover system dependencies while simultaneously managing a breach. The AIBOM can provide the dependency information needed for faster containment.

Organizations should periodically verify that rollback procedures remain practical as systems evolve. A plan created before several new integrations were added may no longer be sufficient.

Include AI in Security Testing

AI security testing should evaluate the complete application rather than only the model. APIs, authentication, access controls, retrieval systems, plugins, service accounts, cloud configurations, and data flows may all introduce exploitable weaknesses. Traditional application security practices remain highly relevant.

Penetration testing can help determine whether weaknesses in surrounding infrastructure provide unauthorized access to AI functionality or sensitive data. AI-specific testing can additionally evaluate prompt injection, excessive agency, information leakage, retrieval authorization, and other emerging threats. Combining both perspectives provides a more realistic assessment.

The AIBOM helps testers understand what components should be included within the assessment scope. Better visibility generally produces more complete security testing.

AI Bill of Materials and HIPAA

HIPAA does not currently require healthcare organizations to maintain a specific document called an AIBOM. However, regulated organizations must conduct an accurate and thorough assessment of potential risks and vulnerabilities to the confidentiality, integrity, and availability of electronic Protected Health Information. AI systems that create, receive, maintain, or transmit ePHI therefore belong within that broader risk-management process.

An organization cannot fully evaluate an AI system without understanding what information it handles, where that information is processed, who can access it, and which vendors or technical components support the workflow. Organizations also need to understand how activity is monitored and how the technology changes over time. An AI inventory provides structured visibility into these questions.

An AIBOM does not automatically make an organization HIPAA compliant. Instead, it provides information healthcare security and compliance teams need to make better-informed risk decisions and document appropriate safeguards.

Final Thoughts

An AI Bill of Materials in healthcare gives organizations a practical way to understand the increasingly complex supply chains behind artificial intelligence. Models, datasets, open-source packages, APIs, plugins, cloud services, permissions, and external vendors can all influence an AI application’s security and privacy profile. Maintaining accurate information about those dependencies makes vulnerability response, incident investigation, change management, and risk analysis substantially more effective.

Healthcare AI will continue changing long after an application is initially approved, making AIBOM management an ongoing process rather than a one-time documentation project. Before organizations can effectively govern AI risk, they first need to understand exactly what their AI systems contain and what those systems can reach. For more practical guidance on healthcare cybersecurity, HIPAA security, AI governance, penetration testing, and patient data protection, follow Tempest Healthcare IT on LinkedIn: https://www.linkedin.com/company/tempesthealthcareit/