Many organisations treat compliance evidence as a quarterly scramble: screenshots, spreadsheets, and manual exports. For AI systems, that approach does not scale. Models change, prompts change, tools change, and policies evolve. Audit readiness needs to be continuous.
Evidence pack automation is the practice of generating a defensible, repeatable set of artefacts from systems of record: telemetry, decision logs, registries, and evaluation pipelines.
Define what an evidence pack contains
An evidence pack typically includes:
- System description. Scope, intended use, and operating model.
- Controls and policies. What controls exist and how they are enforced (see policy layering).
- Change history. Model versions, prompt versions, tool changes, and rollout history (see model registry and prompt change control).
- Evaluation results. Benchmarks, regression suites, and red teaming outcomes (see regression testing and red teaming).
- Operational evidence. SLOs, incidents, and safety metrics (see safety dashboards).
Use telemetry as the evidence backbone
Automation depends on consistent telemetry. Your schema should capture prompt versions, routing decisions, tool outcomes, and policy versions for each request (see telemetry schema).
Where possible, store structured metadata rather than raw prompts to reduce privacy risk while keeping audits feasible (see data minimisation).
Decision logs make controls defensible
Auditors often ask: "How do you know your controls were applied consistently?" Decision logging answers that by recording reason codes for routing, redaction, tool authorisation and policy checks (see decision logging).
Automate summaries, not just raw exports
Evidence packs should be readable. Useful summaries include:
- Top incident types and remediation trends (see incident response).
- Coverage of evaluation suites and recent pass rates.
- Changes in safety SLIs and error budget burn (see SLO playbooks).
- Evidence of deletion and retention compliance (see retention and deletion).
Make packs reproducible
A key attribute of good evidence is reproducibility: given the same time window and the same system version, the pack should regenerate identically. Version the pack generator and pin data sources.
Automated evidence packs reduce audit pain, but they also improve operations: teams debug faster and understand what changed. This is governance that helps delivery, not just governance that slows it down.