AI Governance and EU AI Act | Anexia Digital Engineering

Our offering is aimed at CTOs, security officers and product leads with several AI applications. For companies in Germany, Austria and Switzerland, we plan AI governance based on the existing systems and the collaboration required.

When designing AI controls, we begin with a manageable inventory: which applications use models, which data is processed and who decides on their use? This produces concrete responsibilities and permitted forms of use. A general policy alone does not yet answer whether a specific application may process customer data or prepare binding decisions. These questions are clarified and documented for each use case.

When implementing AI operating rules, we connect requirements with verifiable measures. These include access rules, approved data sources, version records, evaluation requirements and a defined way of handling problematic results. For AI governance, we also define which changes trigger a new review. A new model, an additional data source or a tool with write access can change the previous risk scope. The technical documentation supports the responsible specialist functions in their assessment, but does not replace an individual legal classification. As a result, AI governance gains a practical process that covers an application's idea, approval, change and decommissioning. Responsibilities remain visible after the pilot as well.

Scope of services for AI governance

The agreed scope for designing AI controls can include the following results: application inventory, risk classification, data rules, approval procedures, model register and change records. For each significant result, we define what it is for, who reviews it and in what form it is handed over. This allows development, operations and procurement to assess the same service. Vague umbrella terms in the proposal are replaced by concrete work packages.

Implementing AI operating rules also includes coordinating the necessary interfaces with your team. We clarify access, contacts and available testing options before the relevant implementation steps. Changes to the scope are described together with their impact on effort and sequence. This gives you a clear overview of delivered results, outstanding prerequisites and additional requests.

The key technical decision in AI governance

The depth of control follows the impact of an error. An internal drafting assistant requires different approvals than an application with consequences for customer decisions. For AI governance, we document the relevant assumptions and explain the reasons for the chosen option. Significant alternatives are considered against the same business requirements. This makes later adjustments easier when load, data volume or organisational responsibility change.

A sound decision also takes maintainability and handover into account. When designing AI controls, we examine which skills are available internally and what additional support is needed. This connects technical possibilities with a realistic operating model. The client can see which tasks will arise regularly after the project and which decisions remain with them.

A policy without technical implementation leaves shadow usage in place and merely creates additional documentation effort. When implementing AI operating rules, we treat this point as a concrete review question. A risk is linked to the affected process, its possible impact and a suitable measure. Where prerequisites are still missing, the statement remains explicitly open until the necessary investigation has taken place.

We carry out spot checks to see whether inventoried applications, the models actually used and technical access rules match. For AI governance, we record the initial situation, the tested configuration and the result. Acceptance refers to the agreed scope. Open limitations are documented visibly and assigned to an owned next step. This allows your team to distinguish between a proven function, a conscious decision and an assumption that has not yet been confirmed.

Project example as an illustrative scenario

The following scenario is an application example and not a customer reference. Several business units are trying out AI tools, while IT only learns about data flows and ongoing costs after the fact. In AI governance, the first step would be an understandable register of the applications actually in use. Purpose, processed data, technical providers and responsible persons would be decisive. A mere catalogue of all conceivable risks would not yet improve concrete control.

For AI governance, approvals would be tied to traceable characteristics. An internal writing aid would have different review requirements than a system that provides customer information or triggers business transactions. For relevant applications, the team would define which evidence is required before introduction and after significant changes. The pilot could run through a complete approval procedure, covering the application and the operational handover. It would be necessary to check whether those responsible can be reached, open questions are decided and the documentation can be maintained in day-to-day work.

In this scenario, the result of designing AI controls would be a usable decision-making process with clear roles, concrete review questions and documented exceptions. Existing specialist functions would be involved, especially where legal or data protection assessments are required. A technical rule alone would not replace these assessments. After the trial, it would be possible to see which controls actually help and where unnecessary duplication or unclear responsibilities delay the rollout.

Our experience and our engineering approach

We combine custom software development with the technical tasks of introduction and further development. For AI governance, we bring the perspectives of application, integration and operations together in a shared workflow. Business requirements remain connected to the technical decisions. You work with a defined scope of services and traceable results.

Our experience should become concretely verifiable for your project. That is why, in the technical exchange, we explain how we examine the relevant risks, which work results are planned and how your team can assess them. For designing AI controls, we align the roles involved with the actual need. Available references are presented with their respective project and company context; confidential customer details are not used as general promotional claims.

Effort for designing AI controls

The number of applications, organisational responsibilities and the quality of existing inventories determine the rollout effort. We therefore calculate AI governance on the basis of a defined scope and explicitly stated prerequisites. An initial analysis step can make sense if essential technical information is still missing. Work packages and acceptance criteria can then be agreed on a more reliable basis.

AI governance can be organised as a limited project, as a joint implementation with your team or with subsequent support. Rights of use, access, documentation and responsibilities are clarified in the contract. Ongoing operating hours, response times and external consumption costs are subject to separate agreements. This keeps it clear which services are provided once and which tasks arise regularly after the rollout.

Boundaries of AI governance

The technical implementation supports the business and legal functions, but does not replace a binding legal classification of an AI system. When implementing AI operating rules, we already set out this boundary in the proposal. Required additional services are included together with their business rationale and are not silently assumed. Your team can therefore plan suitable internal skills and divide the overall work sensibly between those involved.