Trusted AI Bill of Materials (TAIBOM)

• TAIBOM is a transparency mechanism that inventories and attests to all critical AI system elements including data, code, models, and configurations.
• It employs structured dependency modeling and cryptographic signing to secure integrity propagation and trace dynamic relationships within AI supply chains.
• TAIBOM enhances system assurance, regulatory compliance, and security by enabling automated verification and robust trust attestation across the AI lifecycle.

A Trusted AI Bill of Materials (TAIBOM) is a transparency and assurance mechanism that explicitly inventories, links, and attests to all critical elements—data, code, model artifacts, configuration, and lineage—across the lifecycle of an AI-enabled system. TAIBOM frameworks expand beyond conventional Software Bills of Materials (SBOMs) to address the particularities of data-driven, dynamic, and loosely coupled AI supply chains. They incorporate structured data models, cryptographically verifiable integrity propagation, trust attestations, and provenance tracking to advance assurance, security, compliance, and system trustworthiness in the AI context (Safronov et al., 2 Oct 2025).

1. Structured Dependency Modeling for AI Supply Chains

TAIBOM introduces a hierarchical, object-oriented model for representing the complex, evolving interdependencies between AI system artifacts. This model explicitly distinguishes between:

• Data objects: Parent class Data, with TrainingData (raw, annotated, or partitioned), grouped into DataPack collections, with cryptographically signed versioned instances.
• Code objects: Parent class Code, extended by TrainingCode and InferencingCode. These retain SBOM references, hash-based integrity, and provenance metadata.
• AI system objects: AISystem as the parent, with subclasses such as TrainedSystem and InferenceSystem. A TrainedSystem links to its source DataPack, TrainingCode, and resultant Weights and Config. Each linkage is cryptographically signed to prevent undetected tampering.

The model allows explicit graph-based representation of dependencies and dynamic relationships. For example:

$$\text{TrainedSystem} \longrightarrow \left\{ \text{DataPack}, \text{TrainingCode}, \text{Config} \right\}$$

$$\text{DataPack} \longrightarrow \left\{ \text{TrainingData}_1, \text{TrainingData}_2, \ldots \right\}$$

$$\text{Config} \longrightarrow \text{metadata and parameters}$$

This formalism enables accurate forward and backward tracing through the dependency graph, supporting fine-grained impact analysis and supply-chain provenance (Safronov et al., 2 Oct 2025).

2. Integrity Propagation and Cryptographic Binding

An essential innovation in TAIBOM is integrity propagation via cryptographic signing and versioning:

• Component Hashing: Each critical artifact—datasets, source/binary code, configuration, model weights—is hashed ($H(\text{component})$), forming the basis for all integrity validation.
• Digital Signatures: Every component (and manifest) is digitally signed. Any modification to an upstream component (such as a data update or code change) invalidates all downstream integrity assertions, triggering security and assurance checks.
• Versioned Lineages: The hash and signature, along with temporal metadata, are propagated through subsequent artifacts. For example,

$H(W) = f( H(C), H(\text{Config}), ... )$

where $W$ is the model weights, $C$ the training code, and $f$ an explicit trusted linking function.

This approach extends SBOM-style transparency mechanisms to account for AI-specific attributes such as continuously retrained weights, evolving datasets, and highly modular pipelines. Runtime artifacts such as serving endpoints and on-the-fly quantizations can similarly be incorporated for end-to-end integrity verification.

3. Trust Attestation and Verification Processes

TAIBOM implements a trust attestation process that ensures reproducible, cryptographically verifiable, and auditable provenance for every link in the AI system supply chain:

• Initialization and Artifact Signing: On creation, each artifact is hashed and digitally signed. The signing includes full provenance (source URI, timestamp, license, and compliance details).
• Linked Propagation: New or derived artifacts (e.g., trained models, processed data) must embed the signed hashes of all direct ancestors—ensuring a robust chain-of-trust.
• Lifecycle Verification: At each operational phase (integration, inference, update, audit), integrity checks compare current artifact hashes against the attested signed values. Any discrepancy triggers immediate alerts and trust revocation.
• Automated Attestation Flow:

[Start] ↓ [Artifact Generation] ↓ (compute H(artifact)) [Digital Signing] ↓ [Propagate to Dependent Artifacts] ↓ [Verification at Deployment/Audit] ↓ [Validation or Trust Failure]

This process supports automated supply-chain validation, third-party audits, and cross-organizational compliance attestation. It is especially effective in distributed or federated AI development settings (Safronov et al., 2 Oct 2025).

4. Comparison with SPDX, CycloneDX, and SBOM Standards

TAIBOM addresses gaps left by existing software supply-chain transparency standards:

Aspect	SPDX/CycloneDX	TAIBOM
Data-model Scope	Software packages, CVEs	Data, code, models, configs, lineage
Cryptographic Links	Metadata only	Signed hash and provenance graphs
AI Semantics	N/A (not AI-aware)	Data/model evolution awareness
Dynamic Updates	Static artifact focus	Tracks retraining, mutable datasets
Vulnerability Trace	At the package level	Propagates across full AI pipeline

For scenarios such as data poisoning detection, post-deployment tampering, or regulatory traceability, TAIBOM’s provenance propagation and integrity binding enable forms of system trust unattainable via standard SBOMs (Safronov et al., 2 Oct 2025).

5. Impact on Assurance, Security, and Compliance

TAIBOM provides direct benefits to multiple assurance domains in AI-enabled systems:

• System Assurance: Enables reproducibility and backward tracing of model outputs to input data and code.
• Security: Ensures that unauthorized changes anywhere in the supply chain are detected immediately. Attests against both insider and supply-chain attacks.
• Regulatory Compliance: Provides timestamped, versioned records and chain-of-trust suitable for audit by external regulators (e.g., EU AI Act, FDA, sectoral standards).
• Incident Response: Facilitates rapid investigation and rollback in response to detected anomalies (e.g., data poisoning, compromised training environments, regression in performance).

These capabilities underpin robust “continuous compliance” workflows for AI, not just point-in-time certification.

6. Use Cases and Operational Scenarios

Concrete uses of TAIBOM include:

• Data Poisoning and Tampering Detection: Enables root-cause analysis if inference errors are traced to compromised training data or code changes, as cryptographic inheritance reveals where integrity was breached.
• Model Rollback and Update Management: Automated verification ensures that only attested model versions are deployed. Any drift or unapproved modification results in trust withdrawal or forced remediation.
• Impact Analysis of Vulnerabilities: If an upstream CVE is disclosed in a library or a data source, all downstream models and deployments inheriting that component can be algorithmically flagged for risk review.

Empirical case studies highlight TAIBOM’s superior provenance tracking compared with SPDX, CycloneDX, and Model Card–style documentation (Safronov et al., 2 Oct 2025).

7. Future Directions and Limitations

TAIBOM sets a precedent for next-generation, cryptographically rigorous supply-chain transparency in AI; however, future directions noted in the paper include:

• Standardization and Ecosystem Alignment: Mapping TAIBOM’s semantics onto evolving international standards, ensuring interoperability with established SBOM infrastructure.
• Tooling and Adoption: Developing scalable, developer-friendly tools for artifact signing, attestation linkage, and automated verification.
• Dynamic and Federated Workflows: Addressing challenges in rapid model adaptation, cross-organization model sharing, and policy-driven partial disclosure (for privacy, business, or regulatory reasons).

Although TAIBOM advances AI transparency and trustworthiness, broad adoption will depend on robust tool support, active standardization, and alignment with both regulatory and sectoral needs.

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TAIBOM thus defines a comprehensive, cryptographically rigorous, and dynamically verifiable inventory of all AI system artifacts and their dependencies. It fills critical assurance, security, and compliance needs that existing software-centric SBOM standards cannot address, providing a foundational structure for trustworthy and auditable AI system deployment (Safronov et al., 2 Oct 2025).