Open Data Infrastructure AI Audit Packet Design

AI auditability fails when every piece of evidence lives in a different tool with a different name for the same thing.

Audit packets need a common spine

An AI system review usually needs source data, catalog state, policy decisions, lineage, model or tool behavior, evaluation evidence, operational telemetry, and owner approval. If those artifacts are collected only after the review starts, the team is already behind.

An audit packet is a designed bundle of evidence. It should not be a folder of screenshots. It should connect the data source, catalog metadata, policy decision, lineage path, context retrieval, tool call, evaluation result, incident notes, and owner review through common identifiers.

AI risk review depends on data evidence

The NIST AI Risk Management Framework gives organizations a structure for managing AI risk. W3C PROV and OpenLineage give teams provenance and lineage concepts for connecting evidence.

ODI makes the packet practical. The system should know which source version was used, which catalog state was active, which policy decision allowed access, which lineage path produced the data, which tool call ran, which evaluation accepted the behavior, and which operator reviewed the exception.

Patterns that work

• Define required packet fields for source, catalog, policy, lineage, retrieval, tool call, evaluation, and owner review.
• Use shared IDs so evidence from different systems can be joined without manual guessing.
• Capture the packet during normal operation, not only during audits.
• Attach exceptions, denials, stale-context events, and compensating actions to the same packet model.
• Test whether a reviewer can reconstruct the answer path from packet data alone.

For adjacent ODI context, read ODI governance operating models for AI, AI platform incident response, and context graphs for regulatory evidence.

What breaks first

• The team collects logs, screenshots, and tickets with no common identifiers.
• Policy evidence and lineage evidence exist, but nobody can connect them to the answer.
• Evaluations verify model behavior without naming the data state behind the test.
• Audit preparation becomes a manual reconstruction project every quarter.

Questions to ask

• Which evidence fields are required for every AI audit packet?
• Which systems produce the source, policy, lineage, telemetry, and evaluation evidence?
• Can packet IDs connect data events to model or tool behavior?
• Can an external reviewer understand the packet without access to tribal knowledge?

Sources to start with

These primary sources anchor the technical claims in this guide.

• NIST AI Risk Management Framework
• W3C PROV overview
• OpenLineage documentation

The audit packet is where AI governance stops being a scavenger hunt.