Life Insurance · Regulated insurer, DORA and AI Act context
Compliance-ready AI hosting for a life insurer
Starting point
A German life insurer wanted to integrate an AI platform for case processing into its operations. The regulatory landscape was complex: DORA requires ICT risk management for outsourced services, BaFin supervisory practice demands proven cloud governance, the EU AI Act sets requirements for audit trails and human oversight, and GDPR Article 9 determines the architecture for medical data. The central question was not only technical — it was regulatory: which operating model satisfies all five frameworks simultaneously without compromising time-to-market?
What we did
We evaluated three operating models for the AI platform: external data centre, dedicated cloud environment, and client-owned cloud tenant. For each model, we produced a full assessment across five regulatory frameworks — DORA, BSI C5, GDPR, EU AI Act, and ISO 27001. The core deliverable was a compliance inheritance analysis: a systematic mapping of which regulatory evidence obligations are covered by the infrastructure platform and which remain with the insurer and the operator. This was accompanied by a hosting recommendation, cost structure, and a responsibility delineation within the shared-responsibility model. Team: 2 people over 6 weeks. Disciplines: cloud architecture, regulatory analysis, compliance documentation.
Results
5
regulatory frameworks analysed (DORA, BSI C5, GDPR, EU AI Act, ISO 27001)
3
hosting scenarios evaluated and documented
6 weeks
from first meeting to pilot launch
100%
of infrastructure-level controls covered by compliance inheritance
What we learned
Most insurers underestimate how much regulatory groundwork a well-chosen infrastructure eliminates. Compliance inheritance is not a marketing term — it is an operational lever. Choose the right platform and you reduce the scope of your own evidence obligations by 60–70% at infrastructure level. Ultimate regulatory responsibility stays with you — but the burden of proof shifts.
This is the summary. How we approached it methodologically — which architectural decisions we made, what we discarded and which patterns can be transferred to other contexts — we discuss in a personal conversation.
Not because we want to sell you something. But because this depth is what our clients engage us for — and it does not belong on the open internet.
More case studies
Insurance
AI platform for claims assessment in insurance
AI platform for claims assessment taken from concept to integration approval in 12 weeks.
Read case studyFood & Ingredients
AI Portfolio Management at a Food Corporation
49 use cases identified and EUR 2-5m annual savings potential validated.
Read case studyInsurance
Disability Claims Review: AI PoC with 98% Extraction Accuracy
98% extraction accuracy across 187 document classes — validated technically, economically, and for regulatory compliance.
Read case study