Not just high-risk AI: Why all AI applications need governance

All AI applications require sufficient governance.
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Marcus Belke

CEO of 2B Advice GmbH, driving innovation in privacy compliance and risk management and leading the development of Ailance, the next-generation compliance platform.

Clear responsibilities, traceable evidence and fixed processes should be defined for every AI used in the company. Ensuring order at an early stage protects data, brand value and innovative strength. The first step here is a central AI inventory, binding workflows and model maps - for all AI applications used in the company.

AI applications need control

AI tools often start out as little helpers in day-to-day work that are designed to make processes more efficient. For example, a team tests an LLM to shorten meeting notes. This saves time until confidential passages emerge outside the company a few weeks later. Nobody wanted to cause any damage. And yet a security incident has occurred.

Such scenarios show how quickly operational helpers can become compliance risks. Especially if they are used without documented approval. Even small applications and supposedly helpful tools can affect key areas of the company:

  • Data: Confidential information can be leaked unintentionally.
  • Decisions: Automated scoring can evaluate applicants unfairly.
  • Liability: Incorrect results can have legal consequences.
  • Reputation: Even a minor incident can damage the trust of customers and partners.


The problem is usually not the technology itself, but the lack of a framework. Who uses the tool? What data is processed? Is there a release? As long as these questions are not answered, the risks remain invisible until the worst comes to the worst.

The consequence: not only large high-risk AI requires control. Even small everyday tools need to be recorded in an inventory with clear responsibilities and an approval process.

Our practical tip: Link every AI application with yours List of processing activities (RoPA). This allows you to recognize immediately whether personal data are used and whether a Data protection impact assessment (DSFA) is necessary.

Regulatory obligations for all AI classes

The AI Regulation distinguishes between different risk classes:

  • Unacceptable risk
  • High risk
  • Limited risk
  • Minimal or no risk


High-risk systems in particular are subject to strict requirements. However, binding obligations also apply to seemingly harmless applications: Transparency about purpose and use, comprehensible Documentation as well as evidence of data, risks and responsibilities.

These requirements apply to all classes of AI and ensure that every AI in the company remains verifiable and auditable. Generative AI is also covered, for example with disclosure obligations for automated content creation (Art. 52 EU AI Regulation).

Those who ignore these requirements risk severe penalties of up to 35 million euros or 7 % of global turnover. Management teams must be able to prove at any time with what purpose, with what data and under what controls AI systems are operated.

It is therefore not enough to have a guideline. We need concrete evidence that shows: Which model is being used? What data is it based on? What limits are known? Who is responsible?

Model maps are the central tool for this, as they document purpose, data sources, versions, performance values, known risks and Responsible persons. This means that questions from supervisory bodies, auditors or management can be answered immediately.

Reading tip: Model maps - why they are so important for AI documentation

What the management should do now

For board members and management teams, it is now a matter of understanding governance not as a control instrument, but as a management task. These four steps help to manage AI in a structured, comprehensible and future-proof manner:

1️. Central AI inventory
Record all use cases systematically with details of purpose, data, operators and responsible parties. This creates Transparency and forms the basis for governance. In Ailance, this inventory can be seamlessly integrated into IT asset management.

2️. Risk assessment with data protection trigger
Assessments of AI applications should be carried out according to a standardized scheme: What are the risks to data quality, fairness, security and Compliance? As soon as personal data are processed, a Data protection impact assessment started. This creates documented evidence that stands up to audits.

3️. Model maps and Documentation
Every AI application needs a profile. Model cards record which data and processes are used, which version is active, which limits exist and who is responsible. This not only creates evidence, but also Transparency for specialist departments and management.

4️. Approvals, monitoring, re-audits
The role-based workflows link the areas of Data protectionIT, Legal and the specialist departments. Each approval is documented in the system with a time stamp and the person responsible. Dashboards show the status and remind users of re-audits. This turns governance into a continuous process rather than a one-off project.

Market environment for AI applications: Act now

The market for Responsible AI is growing rapidly. By 2028, analysts expect an investment volume of over 600 billion USD (IDC study "IDC's Worldwide AI and Generative AI Spending"). Growth is similar to the dynamics before the introduction of the GDPRbut is more global and more urgent.

Companies are faced with a double challenge: On the one hand, they have to implement regulatory requirements such as the EU AI Regulation; on the other hand, they have to gain the trust of customers, investors and partners.

Those who invest in governance now therefore benefit twice over: risks are reduced and the organization can act with demonstrable responsibility. Governance thus turns from a mandatory program into a competitive advantage.

Link tip: Control all AI projects centrally, audit-proof and legally compliant with Ailance AI Governance

Why Ailance is the right lever for all AI applications

The key question is: Can we take responsibility for what our AI applications do - and can we prove it?

An inventory, model maps, risk-based workflows and repeated audits form the backbone of responsible AI. Those who start today will reduce their Liability, strengthens its innovative power and increases the measurable ROI of its AI initiatives.

Ailance starts right here:

  • Operationalization: Rules become workflows. This means, for example, that there is no approval without complete information.
  • Automation: The Data protection impact assessment starts automatically when personal data are affected.
  • Management view: Dashboards show inventory, risks, approvals and bottlenecks in real time.
  • Scalability: Ailance can be adapted to any size of company, from local top dogs to large global corporations. With the right interfaces, Ailance can be integrated into the corporate network.


The result: processes run more clearly, audits are carried out more quickly and projects can be implemented more reliably. In addition, the management gains a measurable ROI through reduced Liabilityless testing effort and accelerated rollouts.

Request the demo now and experience it for yourself,how Ailance controls all AI applications securely and comprehensibly.

Marcus Belke is CEO of 2B Advice as well as a lawyer and IT expert for data protection and digital Compliance. He writes regularly about AI governance, GDPR compliance and risk management. You can find out more about him on his Author profile page.

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