AI transparency is entering its second phase.
The first phase was about visibility: understanding what components exist inside software and AI systems. The next phase is about governance: proving accountability, controlling disclosure, and maintaining trust over time.
The recently published OpenChain AI System Bill of Materials guidance makes this shift explicit. It reframes AI SBOMs not as deliverables, but as part of a broader organizational system of responsibility.
This article explores what that shift means in practice — and why AI governance cannot be solved with artifacts alone.
Unlike traditional software, AI systems are not static. Models evolve, datasets change, licenses shift, and fine-tuning introduces new dependencies long after deployment.
Treating AI SBOMs as one-time exports creates a false sense of control. What regulators, customers, and security teams increasingly expect is continuous traceability across the AI lifecycle.
This is where governance - not generation - becomes the core challenge.
The OpenChain AI SBOM guidance is intentionally not prescriptive about tooling. Instead, it focuses on organizational questions:
In other words, OpenChain treats AI SBOMs as evidence - not the objective itself.
This framing aligns closely with how modern supply-chain security and compliance programs actually operate.
While governance principles are well understood, most organizations struggle to operationalize them. Common failure modes include:
The result is compliance friction, not confidence. Bridging this gap requires infrastructure - not just guidance.
This is where platforms like Exodos Labs focus their effort.
By extending SBOM governance mechanisms into the AI domain, organizations can move from conceptual alignment to enforceable practice:
Governance becomes something teams do, not something they document after the fact.
| OpenChain AI SBOM Principle | What It Requires in Practice | How Exodos Labs Implements It |
|---|---|---|
| Lifecycle-based transparency | Continuous updates, not static files | Versioned inventories, automated ingestion, change tracking |
| Organizational accountability | Clear ownership and auditability | Immutable audit logs, role-based responsibility, activity trails |
| Policy-driven governance | Enforceable rules, not guidelines | Quality gates aligned with internal and regulatory policies |
| Controlled disclosure | Share what’s necessary, nothing more | Attribute-based access control and redaction |
| Ecosystem interoperability | Standard-aligned, vendor-neutral exchange | API-first architecture and secure inter-company sharing |
| Audit-ready evidence | Proof over time, not point-in-time | Historical records, traceability, compliance reporting |
AI Governance Is an Infrastructure Problem Key paragraph: The OpenChain AI SBOM framework is an important milestone - not because it introduces a new artifact, but because it clarifies the nature of the problem.
AI governance is not a reporting exercise.
It is an infrastructure challenge.
Organizations that treat it as such will move faster, comply earlier, and build more trust - not only with regulators, but across their entire ecosystem.