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    An ongoing journal on standards, regulation, security and artificial intelligence — written from the perspective of operational insurance IT.

    15.07.20265 min read

    Certified, but secure? What ISO 27001 & TISAX truly mean for insurance IT service providers

    Any IT service provider operating in the insurance sector can hardly avoid security certifications. Tenders demand them, clients set them as a contractual prerequisite, and board members often refer to them as proof of "audited security". However, in practice, there is often confusion about which certificate is actually relevant for which purpose – and what a certificate can achieve, but also what it cannot. In particular, the confusion between ISO 27001 and TISAX regularly leads to misunderstandings in the insurance industry.

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    01.07.20265 min read

    DORA in Practice: What Insurers and IT Service Providers Really Need to Consider in 2026

    Since 17 January 2025, the Digital Operational Resilience Act (DORA) has applied directly in all EU member states. For banks, insurance companies, payment service providers, and other financial undertakings, this marked the end of a multi-year preparation phase. However, in 2026, the nature of the regulation has noticeably changed: pure documentation obligation has become active supervision. BaFin has unequivocally declared 2026 a test of endurance – the focus is no longer on whether DORA has been implemented, but how stringently.

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    15.06.20264 min read

    AI Meets BiPRO Standards: Why Structured Interfaces Are Essential for Automated Case Processing

    Artificial intelligence was the dominant topic at BiPRO Day 2026 – and precisely in the anniversary year when BiPRO e.V. celebrated its 20th existence. At first glance, this may seem surprising: Why is a two-decade-old interface standard needed when modern AI systems can supposedly process unstructured data? However, the BiPRO community at the event took a clear opposing stance: Artificial intelligence does not replace standards – it makes them more important than ever. This is because powerful AI systems require structured, quality-assured, and semantically unambiguous data to function reliably.

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