Automating Compliance Evidence Collection with AI

The evidence problem

The EU AI Act's high-risk obligations generate a lot of paperwork: technical files, logging records, post-market monitoring, incident notifications, human oversight logs. For most organisations, manual evidence collection fails within six months.

Where automation earns its keep

• Model logs (Article 12) — pull from MLOps tooling into a versioned, tamper-evident store.
• Data lineage (Article 10) — snapshot dataset schemas and validation reports at every training run.
• Change history — link every model version to a commit, ticket, and approval.
• Vendor evidence — scheduled pulls of updated model cards and conformity declarations.
• Literacy records (Article 4) — LMS webhook into a central register.

Pattern: capture at source, store immutably, index for retrieval.

Where automation makes things worse

• Risk classification — it is a legal call.
• Human oversight sign-offs for high-risk decisions.
• Root-cause analysis — automate the ingestion, keep the analysis human.

A reference architecture

1. Ingestion layer — webhooks from MLOps, HRIS, LMS, ITSM, vendor portals.
2. Evidence store — object storage with write-once semantics.
3. Metadata graph — every artefact linked to system, obligation, and accountable person.
4. Query layer — dashboards plus a regulator-ready export.
5. Retention — Article 12 logs for at least six months.

AI in the loop (carefully)

LLMs are useful for draft-then-review: technical file sections, incident summaries, complaint clustering. Every draft reviewed by a named human before it enters the store.

Success metric

A regulator's information request answered in hours, not weeks.

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Related: Custom compliance automation · EU AI Act overview · Free readiness assessment