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Data Flow by Data Class

Six classes of data, each traced from where it is produced to where it finally rests.

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Produce Transport Land Serve Retain Definitions Git commit Pipeline · on merge Azure SQL Signed bundle All versions kept Execution state Orchestrator step Checkpoint · sync Cosmos DB Query and replay 90 d then archive Prompts and output Model call Gateway capture ADLS · encrypted Trace deep link Off by default Memory and knowledge Source system Ingestion · nightly AI Search index Trimmed retrieval Follows source Telemetry and cost OTel span Event Hubs Monitor + ADX Grafana · console 90 d hot · 2 y cold Audit Privileged action Append only Immutable blob Sentinel + export 7 years · WORM Data Flow by Data Class — Produce, Transport, Land, Serve, Retain Application we own Interface / broker Data store Security / platform External / third party Queue / topic Prompt and completion capture is opt-in per project and defaults off. Everything else on this page is always on. v 1.0 · owner Data Architecture · date 2026-08

Decisions

  • Prompt and completion capture is opt-in per project and defaults to off; the platform is useful without it
  • Telemetry and audit are always on and cannot be disabled by a tenant — they are what makes the platform governable
  • Definitions flow only one way, from Git through the pipeline into the registry; there is no console path that bypasses it

Cadence

  • Checkpoints are synchronous and on the critical path; telemetry is asynchronous and never is
  • Knowledge ingestion is nightly by default, with an on-demand path for a corpus that changes intra-day
  • Cost facts roll up hourly; the live budget check reads the reserved-token counter in Redis, not the rollup

Assumptions

  • Source systems can be crawled with an identity that reflects end-user ACLs, so security trimming has something to trim on
  • Legal has signed off the default retention periods per class; a tenant may shorten them but not extend beyond the organisation ceiling