Is your AI strategy really secure, audit-ready and legally safeguarded?

An effective starting point is a focused risk assessment that combines technical, legal and organizational aspects. We recommend first mapping the data flows and target use cases: where does the data come from? Which systems access it? Which outputs are critical? These questions create clarity about the attack surface.

In parallel, a PIA (Privacy Impact Assessment) should be carried out to identify and prioritize data protection risks. A PIA is particularly important when personal data or sensitive operational data are involved. It provides concrete measures and helps involve the data protection officer.

Technically, we review access rights, logging, network boundaries and model hosting options. A proof-of-concept for secure self-hosting or isolated environments helps identify real limitations. The goal of the first assessment is a prioritized roadmap with clear milestones and responsibilities.

Practical tip: involve stakeholders from legal, security, IT and the business unit early. Without shared prioritization an assessment often becomes a non-binding list. With clear KPIs and responsibilities, however, it creates urgency and progress.

Data governance is the backbone of secure AI systems. Without clear classification, retention policies and lineage, neither auditability nor responsible model operation is possible. Governance defines which data may be used, who has access and how long data should be retained.

We implement automated classification mechanisms, document data provenance and transformations, and set up retention workflows. This makes it possible, for example, to reconstruct training datasets and trace decisions during audits — a decisive factor for ISO or TISAX checks.

Data governance also reduces technical risks: by separating PII from operational data early, you minimize leak risks and simplify the implementation of self-hosting options. Governance also enables clear assignment of responsibilities between data owners, data stewards and engineering teams.

In short: good governance is not bureaucratic overhead but an enabler for robust, scalable and auditable AI operations — and thus a core component of any security strategy.

Self-hosting makes sense when legal, regulatory or data protection reasons exclude the use of public models or third-party hosted services. It can also be the better choice when there are high requirements for latency, cost control or operational SLAs.

Technically, self-hosting starts with an assessment of infrastructure requirements: compute, storage, network segments and backup strategies. We recommend zone-based architectures in which sensitive workloads run in isolated environments and API gateways provide controlled access.

Security aspects such as secrets management, hardening container images, network policies and regular patch strategies are central. In addition, we implement audit logging and monitoring so that all inputs and outputs are traceable. These measures are necessary to demonstrate compliance.

We build self-hosting solutions iteratively: first a minimally secure setup for testing, then stages toward production readiness with automation and hardening. This avoids a monolithic lift-and-shift and reduces risks during migration.

Auditability is achieved through three pillars: complete data lineage, comprehensive audit logs and reproducible model training runs. We ensure that every change to data, pipelines or models is versioned and documented. This way, decisions can be traced back to the training data used.

For audit logs we implement standardized events: data access, model inference, parameter changes, permission changes and deployments. These logs are stored securely, made tamper-evident and fed into reporting pipelines that auditors can easily review.

In addition, we provide reproducibility artifacts: training manifests, checkpoints, seed configurations and evaluation metrics. These artifacts make it possible to reproduce models with identical configuration, which is often required in regulatory audits.

The organizational side is also important: responsibilities, reviews and change-approval processes must be defined. Without clear governance, the best logging is of little use because responsibilities are missing.

Red-teaming goes beyond classic penetration tests: it simulates targeted attacks on model behavior, prompt injection, data spoofing and manipulation of outputs. The goal is to identify weaknesses in the interaction between model, input processing and the environment.

A typical red-team engagement includes adversarial attacks, input fuzzing, prompt manipulation tests and scenarios for data exfiltration. We analyze how the system reacts to unexpected or malicious inputs and whether outputs could trigger incorrect actions.

The frequency depends on risk and the rate of change: for stable systems a comprehensive test every six to twelve months is sufficient; for rapidly iterating models we recommend quarterly checks. After any significant change to the model, data pipelines or permissions, a targeted test should be performed.

It's important that red-teaming becomes a continuous process: test results must lead to improvements, vulnerabilities must be closed and tests repeated. Only then does real resilience emerge.

We work pragmatically with established standards such as ISO 27001, NIST frameworks and industry-specific schemes like TISAX. Our goal is to translate the requirements of these standards into concrete technical and organizational measures that can be proven automatically.

In certification projects we support gap analyses, preparation of required documentation and the implementation of audit-relevant controls. We provide templates, policies and technical artifacts that auditors can inspect directly.

Our approach is pragmatic: we prioritize measures by risk and feasibility and implement the low-hanging fruit first to achieve quick compliance improvements. In parallel we build out longer-term elements such as governance programs and automation.

In the end we accompany the audit, provide the technical evidence and ensure that your organization is not only audited but remains continuously audit-ready.