Open Data Infrastructure
Foundation for AI Data Control Plane Runbooks
How data control plane runbooks handle access failures, stale context, policy drift, catalog outages, and AI-facing incidents.
A control plane that has no runbook is just a diagram waiting for its first incident.
AI infrastructure needs operational instructions
A foundation for AI needs a data control plane that can coordinate access, policy, metadata, lineage, quality, cost, and context. That sounds architectural. It becomes useful only when operators know what to do when something breaks.
Runbooks turn the control plane into operating practice. They define what to check when access fails, context is stale, policy drifts, catalog service degrades, lineage is missing, or an agent returns an answer that cannot be defended.
Core idea: A data control plane runbook connects architecture to incident response, owner review, and evidence collection.
Control evidence has to be observable
The OpenTelemetry documentation describes telemetry signals for observing systems. OpenLineage documentation gives teams a way to describe lineage events across jobs and datasets. The Open Policy Agent decision log documentation covers policy-decision evidence.
The runbook should tie those evidence sources together. An AI access failure might involve identity, catalog grants, policy decisions, source freshness, query execution, retrieval ranking, and answer generation. Operators need a path through those layers, not a pile of dashboards.
Patterns that work
- Write separate runbooks for access failure, stale context, policy drift, catalog outage, and disputed answer.
- Name the evidence source for identity, policy, lineage, freshness, query, retrieval, and answer review.
- Include escalation owners for data products, platform services, governance, and AI applications.
- Capture the control-plane state at incident time before cleanup changes the evidence.
- Review runbooks after incidents and after major control-plane architecture changes.
For adjacent ODI context, read foundation for AI control plane architecture, ODI control planes for AI workloads, and AI platform incident response.
What breaks first
- Operators know the control-plane components but not the incident sequence.
- Policy logs, lineage records, and query telemetry use different IDs with no bridge.
- A catalog outage has a recovery plan but no AI context impact review.
- The incident review starts after evidence has already been overwritten or expired.
Questions to ask
- Which runbook handles each control-plane failure mode?
- Where are policy, lineage, catalog, telemetry, and context evidence joined?
- Who owns escalation across data, platform, governance, and AI teams?
- Can the team replay the control-plane state that shaped a disputed answer?
Sources to start with
These primary sources anchor the technical claims in this guide.
The control plane is only as real as the runbook people can follow under pressure.