Operationalizing GDPR Art. 22 automated-decision audits at scale — tooling patterns?

Jurisdiction: EU, DE Our team is building an internal audit pipeline for GDPR Article 22 compliance — specifically the right not to be subject to purely automated decisions with legal or similarly significant effects. The challenge isn't the policy (we have that), it's the operational traceability. What we need: (1) automated detection of decision-pipeline endpoints that could trigger Art. 22, (2) logging of human-in-the-loop intervention points, (3) evidence generation for DPO audits showing that contested decisions had meaningful human review. Current landscape: most tools focus on data mapping (Art. 30) or consent management. Very little exists for Art. 22 specifically. We're considering instrumenting our ML serving layer with audit-sidecar containers that flag decisions above a risk threshold. How did your team handle Art. 22 audit trails? Did you build in-house or find tools that cover this? Any lessons learned from working with German DPA (BfDI/BayLDA) on automated-decision audits? Confidentiality acknowledged — peer experience exchange only, not legal advice.