When you need this
- Enterprise security questionnaire blocked deal
- Legal asked for technical documentation you do not have
- High-risk AI system needs monitoring evidence
- Need a repeatable quarterly evidence export
Prerequisites
- Model registry or change log
- Eval and monitoring outputs
- Named evidence owner per system
Tools
Start with registry + experiment tracking before full deployment automation.
Self-host for regulated workloads; cloud for faster pilot.
Steps
- 1
Inventory high-risk systems
Map each AI system to owner, data sources, and decision impact. Legal validates classification; engineering owns artifact collection.
- 2
Map artifacts to Annex IV sections
Architecture diagrams, training data summary, eval results, monitoring plan, change history, human oversight description.
- 3
Automate collection where possible
Quarterly export from MLflow, Langfuse, and CI eval gates into a versioned evidence folder. Prefer links to systems over static PDFs.
- 4
Gap analysis in assessment
Score completeness; prioritize engineering fixes (missing evals, missing owners) over PDF polish.
Adoption pitfalls
- Legal writes docs that diverge from how systems actually run
- One-off PDF dump with no refresh cadence
- No link from live model version to evidence pack
Adoption checklist
- Evidence owner named per system
- Quarterly export automated
- Gaps tracked in engineering backlog
- Post-market monitoring plan documented
SEER REAL assessment / sprint
EU evidence readiness is a core assessment output. Sprints close the top engineering gaps (registry, evals, monitoring) before the next questionnaire cycle.