Trust Boundaries: Provenance Without Data Exposure
An AI Bill of Materials (AIBOM) becomes a governance mechanism when an AI workload runs where the data cannot simply be copied, inspected by everyone, or released with its outputs. This chapter focuses on that boundary: how a Trusted Research Environment (TRE) can preserve model, data, execution, and output provenance while keeping sensitive inputs under controlled access. The practical judgment is not whether an AIBOM contains every possible detail, but whether it lets an authorised reviewer verify the relevant relationships without confusing recorded controls with permission, safety, or legal approval.
The need is clearest in sensitive analytics. Health, finance, national-security, and other research workflows often have to demonstrate how an output was produced while limiting disclosure of the underlying information. A conventional documentation process tends to separate these concerns: one log describes the job, another system controls access, and a reviewer tries to connect them later. The SACRO-aligned design described in the Frontiers study brings the connections into the AIBOM itself. It places provenance capture inside the controlled execution path and treats release conditions as part of the evidence surrounding the result.
The job runs inside a controlled boundary
A TRE is not just a private server. It is an environment in which data access, computation, auditing, and output disclosure are governed together. The source describes TREs as important for secure analysis of sensitive data, while also identifying a practical gap: software assets used in analytic workflows are often not formally recorded and validated. That gap matters because a researcher may be able to show that an analysis ran inside an approved environment without being able to reconstruct which model, library, container, or runtime configuration actually produced the result.
The AIBOM addresses this by putting the capture pipeline alongside the workload. A data processor runs an analytic task inside an isolated job container. The container supplies an execution boundary, while the AIBOM records the relevant environment fingerprint, model identity, dependencies, and execution state. The record can also carry federated lineage when the same kind of analysis is performed across isolated nodes. Instead of asking each site to produce a free-form account after the fact, the design creates a common machine-readable object that can be validated at pipeline boundaries.
The boundary has two directions. Evidence enters the job in the form of an authorised data reference, model artefact, configuration, and software environment. A result leaves only after the workflow has applied the relevant disclosure control and bound the output to the provenance record. The raw data need not cross the boundary merely because a later reviewer needs to know which data version was used. Conversely, a signed AIBOM is not a pass that allows an output to leave. It is evidence for the people and controls that decide whether release is permitted.