Introduction to governance, assurance, and evidence

Purpose of governance, assurance, and evidence

Policies and control objectives are commitments. Assurance is the discipline that turns a commitment into something a reviewer, auditor, customer, or incident responder can actually verify: a named owner, an implemented control, a test result, and a dated evidence record tied to one specific system version.

SAI-250 does not introduce new sovereignty principles. It takes the control objectives from Write effective control objectives and the evidence habits from Evidence by design and turns them into a repeatable assurance and release practice that spans every earlier module.

Start from earlier-module artifacts

The minimum inputs are:

• The system definition, trust-boundary map, and control-objective register from SAI-100 and SAI-120.
• The prioritized threat and residual-risk register from SAI-110.
• Artifact provenance, model lifecycle, and serving decisions from SAI-200, SAI-210, and SAI-220 where those modules apply.
• Identity, gateway, and egress policy decisions from SAI-240.

If a control objective has no named owner or no evidence producer, treat that as a finding to be recorded, not a gap to quietly fill in.

What makes AI assurance different

Conventional assurance already covers ownership, testing, and evidence. AI systems add:

• Probabilistic behavior, so a correctly configured control does not guarantee correct behavior — evaluation evidence matters as much as configuration evidence.
• Fast-changing dependencies such as models, prompts, indexes, and adapters that can silently change system behavior without a conventional code change.
• Controls inherited from platform, gateway, and supplier layers that the AI workload team did not implement and cannot assume are sufficient without independent verification.
• Evidence that must avoid leaking the sensitive prompts, documents, or personal data it exists to help protect.

The SAI-250 method

1. Map obligations and risk decisions to control objectives, owners, and evidence producers.
2. Assess evidence quality: identity, scope, time, source, integrity, and retention.
3. Assemble a versioned AI system passport for the exact release candidate.
4. Run a review proportionate to purpose, impact, and uncertainty.
5. Record exceptions, residual risk, and material-change triggers.
6. Issue an explicit release recommendation with monitoring and rollback conditions.

Module outputs

• A control-to-evidence traceability matrix.
• An ownership and review model.
• A versioned AI system passport.
• A release assurance recommendation.
• An exception, change, and reassessment register.

These artifacts support a decision. They do not replace legal interpretation, an independent audit, or production authorization.