If you sell SaaS in DACH and your product has an AI feature, two rulebooks stack on top of each other. GDPR has been live since 2018. The EU AI Act (Regulation 2024/1689) phases in from February 2025 to August 2027. As of mid-2026, prohibited-practice bans and AI literacy obligations are already enforced, GPAI rules apply, and high-risk system obligations land 2 August 2026. Your build team owns nine artifacts. Your DPO owns four. The CEO signs them.
This is engineering perspective, not legal advice. We have shipped AI features for DACH clients under the GDPR regime and we have re-scoped active builds against the AI Act timeline.
Stacking compliance into your build?
Book Free ConsultationThe AI Act applies in stages. The ones that matter for a DACH SaaS founder running an AI feature:
Germany's SDLC reviewers (BfDI and state DPAs) and Austria's DSB will lead GDPR enforcement; market-surveillance authorities for AI Act enforcement are being designated by each member state through 2026.
The decision tree is short. Your system is high-risk if it falls under Annex III categories: biometric identification, critical infrastructure, education and vocational training access, employment and worker management, access to essential private and public services (including credit scoring and insurance pricing), law enforcement, migration, justice administration, democratic processes. For most B2B SaaS, the high-risk bucket gets hit by CV screening, employee monitoring, credit scoring, and insurance pricing features.
This is the part most legal-led compliance projects get wrong. Compliance artifacts are not Word documents written at launch. They are by-products of how the system is built. The nine the engineering team owns:
Compliance Theater fails because nobody owns the artifact. Here is the table we use in scoping conversations. Adjust roles to your org.
| Control | Applies under | Owner in a 5-person team |
|---|---|---|
| Data Processing Agreement | GDPR Art. 28 | CEO or Founder |
| Records of Processing (Art. 30) | GDPR | Tech Lead |
| DPIA | GDPR Art. 35 | Tech Lead plus external DPO |
| Sub-processor list | GDPR Art. 28(2) | CEO |
| Risk-management system | AI Act Art. 9 | Tech Lead |
| Data governance | AI Act Art. 10 | Data Engineer |
| Technical documentation | AI Act Annex IV | ML Engineer |
| Logging | AI Act Art. 12 | Backend Engineer |
| Transparency to users | AI Act Art. 50 | Product Lead |
| Human oversight UX | AI Act Art. 14 | Product Lead |
| Copyright disclosure (GenAI) | AI Act Art. 53(1)(d) | ML Engineer |
| Incident reporting pipeline | AI Act Art. 73 | Tech Lead |
| Post-market monitoring | AI Act Art. 72 | ML Engineer |

"Compliance design starts at architecture, not at launch. If you cannot point to the line of code that produces the artifact, the artifact is fiction."
The two regimes overlap heavily, and the overlaps are where founders trip:
Most DACH SaaS founders are deployers of foundation models from OpenAI, Anthropic, Mistral, or open-source weights. Deployer obligations are lighter than provider obligations but not absent. If you fine-tune a model and deploy it under your brand, you may become a provider for AI Act purposes, picking up technical documentation, copyright disclosure, and (for high-risk) the full Chapter III stack. Read Art. 25 carefully before fine-tuning, because the regulatory cost step-change at "I am now a provider" is significant.
From Wavect's engagement history on AI-feature builds: stacking the compliance artifacts into an existing SaaS adds 10 to 20 percent to engineering time when retrofitted. Built in from architecture, the marginal cost is closer to 3 to 5 percent. The expensive move is bolting it on at launch. The cheap move is owning the artifacts as code from sprint one. RAG and agent features hit transparency and logging requirements first; MCP tool integrations need the audit trail story before you ship.
GDPR plus AI Act is not double work if you treat compliance as architecture. Records of processing, DPIA, technical documentation, logging, transparency UI, human-oversight surfaces. Each one maps to a line of code, a database table, or a UI component. The legal team writes the disclosures; the engineering team produces the substance.
If you are a DACH SaaS founder reading this in 2026 and your AI feature is shipping, audit yourself against the nine engineering-owned artifacts above. The August 2026 high-risk deadline does not move. The fines are real. But the work is not exotic. It is good engineering documented honestly. That is a discipline a serious team picks up once and reuses on every product.
Building under the AI Act timeline?
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