Why do financial and insurance companies in Dortmund need a robust AI security & compliance strategy?

The regulatory landscape for AI in the financial and insurance sector is multi-layered: in addition to the GDPR, bank- and insurance-specific rules as well as supervisory requirements are relevant. At the national and EU level, AI-specific guidelines are increasingly emerging that demand transparency, risk assessment and governance. In practice, this means companies must document decisions, data flows and responsibilities to remain auditable.

For Dortmund-specific implementations, collaboration with local compliance teams and auditors is also important. Internal rules – such as centralized incident management, defined review cycles and a clear role model for model changes – complement external requirements. In many cases it makes sense to treat audit readiness as a parallel task early in the project.

Practically, we recommend a combination of technical measures (e.g. audit logs, versioning, access controls) and organizational processes (e.g. regular PIAs, responsibility matrices). A Privacy Impact Assessment is often the first step to identify risks and plan mitigating controls.

Concrete measures for the financial sector can include standardized review paths for KYC models, explainability reports for underwriting decisions and regular red-teaming exercises. Implementation should be designed to satisfy both internal and external audits while not unduly restricting operational agility.

Data sovereignty is central for financial and insurance companies because customer data is particularly sensitive. Technically, the solution starts with clear data classifications and tagging: who may see which data, how long may it be stored and where may it be processed? Based on this, data access rules, encryption strategies and retention policies can be implemented.

For operational implementations, self-hosting or VPC-based architectures are suitable to restrict physical data access. Secure self-hosting & data separation includes measures such as tenant isolation, network segmentation, encryption at rest and in transit, and key management via HSMs or cloud KMS with strict access restrictions.

In addition, technical controls such as access control lists, role-based access and attribute-based access are useful to manage granular permissions. Audit logging is another core element: every data query, each model inference event and every data modification should be traceably logged and available long-term.

For Dortmund companies that work with sensitive partners in logistics or energy, a hybrid approach is recommended: critical data stays on-prem or in certified domestic cloud environments, while less sensitive functions run in secure cloud environments. It is important that architecture decisions are documented and regularly reviewed.

Self-hosting offers maximum control over data and infrastructure and is often the preferred choice when regulatory requirements demand strict data locality or full control over keys. In sensitive areas of finance and insurance, self-hosting can help meet audit requirements and minimize data loss.

Cloud hosting, on the other hand, offers scalability, managed services and often built-in security features. Modern cloud providers also enable VPC setups, dedicated hardware modules and compliance certifications that address many regulatory concerns. The choice therefore depends less on a general security characteristic and more on concrete requirements for control, auditability and operational effort.

A pragmatic approach is a hybrid model: core systems and sensitive data remain on-prem or in certified domestic clouds, while batch jobs, development and non-sensitive services run in the cloud. This middle ground allows agility without sacrificing compliance.

In our work we recommend a detailed risk analysis: which data truly needs to remain on-prem? Which business processes tolerate additional latency? Based on this analysis, an architecture is derived that balances security, cost and speed.

Audit readiness is not a finish line but an ongoing process. It becomes efficient when documentation, logging and test results are automated and converted into standardized templates. Compliance automation modules (e.g. for ISO or NIST templates) reduce manual effort and ensure consistency.

Technically, this includes automated reports from CI/CD pipelines that consolidate model versions, tests, security scans and infrastructure changes. On the organizational side, clear responsibilities and regular review cycles are necessary so that documentation remains current and can be credibly presented to auditors.

We rely on living documents that are directly fed from DevOps and MLOps processes: test results, red-teaming findings and PIA outcomes are versioned and annotated with metadata. This creates an audit trail that covers both technical details and management overviews.

Practical tip: involve auditors early. Regular pre-audits and workshops with internal auditors reduce surprises and accelerate formal review processes.

Bias and fairness are central risks, especially for underwriting and pricing models. A first step is thorough data exploration: which variables exist, how are values distributed and which historical biases might be embedded in the data? Based on this analysis, bias risks can be identified and targeted mitigations planned.

Technical measures include data cleansing, controlled sampling, fairness regularization and explainability tools. Even more important are organizational controls: review boards, documentation requirements for feature engineering and transparent model decisions that are understandable to business units.

Regular monitoring mechanisms detect drift and new bias sources in production. These include performance metrics by customer group, continuous tests and alerting on deviations. Red-teaming helps uncover hidden bias cases and simulate real-world impacts.

For insurers the legal dimension is also relevant: discriminatory models not only pose reputational risks but may also lead to regulatory sanctions. A preventive, compliance-backed development process is therefore essential.

Red-teaming is not a one-off security exercise but a targeted test to identify vulnerabilities in model and system architectures. For AI systems this means: deliberate attack scenarios that simulate model inference manipulation, data poisoning or targeted prompt injections.

In finance and insurance, red teams test whether a KYC algorithm can be manipulated, whether sensitive information can be extracted from inference logs, or whether output controls can be bypassed. Results lead to concrete measures: patch plans, monitoring extensions and improved access controls.

Red-teaming should be carried out regularly and insights must flow back into the development process. In parallel, blue teams are responsible for implementing proven defensive measures and ensuring continuous monitoring.

It is important to link red-teaming with compliance requirements: tests must be documented, responsibilities clear and results auditable so they hold up in audits.

The timeline strongly depends on project scope. A focused PoC for technical feasibility can often be completed in 4–8 weeks. This proof of value clarifies whether a use case works technically and what data is required.

The transition to an audit-capable production system includes architecture decisions, security hardening, integration into core systems and creation of compliance documentation. For small to medium projects, 3–6 months are realistic; complex integrations and strict regulatory reviews can take up to 9–12 months.

Key variables are data availability, integration effort, scope of audit requirements and existing infrastructure. A clear project plan with milestones for governance setup, technical implementation and audits significantly accelerates the process.

Our experience shows: those who integrate compliance requirements early instead of retrofitting them at the end reduce time and risk considerably. That is why we work according to the co-preneur principle: we take responsibility and drive both technical and organizational tasks forward simultaneously.