PLAIN: A Sovereign Data and AI Platform for Germany

Germany's federal ministries were sitting on data they couldn't use together. Each department ran its own tools, its own pipelines, its own vendor contracts. When a question spanned departments — pandemic response, climate policy, supply-chain risk — the analysis took months of coordination, if it happened at all. And the usual shortcut was off the table: sovereignty requirements ruled out the hyperscalers, and German public procurement adds physics of its own.

The brief for PLAIN (Platform Analysis and Information Systems): one shared data and AI platform for five federal ministries, run from a sovereign Berlin data centre, operated by Bundesdruckerei under the Federal Foreign Office. Fully accessible under BITV 2.0. No vendor lock-in. Robust enough for everything from crisis monitoring to funding allocation.

Earlier in my career I treated compliance the way most teams do — build the product, then work out how to make it pass. What that ordering reliably produces is late structural surprises: an end-of-cycle review doesn't find cosmetic issues, it finds foundational ones, because the architecture settled months before the rules entered the room.

On PLAIN I inverted it. KRITIS, BSI controls, GDPR, and BITV 2.0 were inputs to the first architecture decision, not tests applied to the last one. That sounds slower and is actually faster: a federal requirement can't be argued down in a workshop, so entire categories of debate never started. The constraint threw out most of the options before we got attached to any of them, and the architecture came out simpler for it. The trade was accepting restrictive defaults up front — sovereign infrastructure only, isolation everywhere, auditable everything — in exchange for never meeting the regulator as an adversary at launch.

A containerised multi-tenant architecture with network and identity isolation per ministry. A data layer combining lake, warehouse, data contracts, lineage, and catalog. Self-service workbenches matched to real roles: Apache Superset for no-code dashboards, GitLab for inner-source collaboration, Jupyter and ML toolchains for data science teams. Virtualised GPUs with quota policies kept AI workloads elastic without runaway costs.

A platform nobody uses is expensive infrastructure with a logo. Analysts, data scientists, and policy advisors each got a workflow shaped for them — governed data onboarding with PII handling, dashboards mapped to ministerial KPIs, reusable container templates that cut use-case rollout from weeks to days. Sandbox tenants with synthetic datasets gave teams room to experiment without touching production data. Cross-ministry sharing ran on inner-source patterns, scoped tokens, and data-sharing agreements, so reuse never meant losing control of your own data.

Version 1.0 reached production in June 2023, roughly eight months after development started — a timeline German public-sector observers tend to read twice. The platform took 2nd place in the eGovernment Competition for "Digital Transformation through AI and Modern Infrastructure."

What runs on it today: pandemic and political crisis monitoring with scenario insights, climate-aware land management planning, supply-chain criticality forecasting, optimised funding programs, BMZ data products with AI-assisted dashboards. Five ministries, one sovereign platform, none of which existed two years prior.