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.
The AI Regulation requires functioning governance structures. AI systems must be identified, evaluated, documented and monitored. In addition, processes must be provided in order to respond appropriately to incidents. This makes AI compliance an operational issue that must be dealt with Data protection or information security. Unlike in these areas, however, many companies still lack the necessary organizational maturity. However, this can already be implemented today.
Reality in many companies: The invisible AI
There is a clear pattern in corporate practice: the use of AI is growing dynamically, while governance is lagging behind. Most organizations underestimate not so much the relevance of regulation as the complexity of its operational implementation.
A key problem is that there is often no complete picture of the AI in use. In addition to official projects, numerous applications are created decentrally, for example through the use of SaaS solutions or generative AI in specialist departments. Without a consistent inventory, however, there is no basis for any kind of control.
In addition, even known systems are often not systematically evaluated. Classification into regulatory categories is done selectively or not at all. As a result, it is unclear which specific requirements apply at all.
The lack of comprehensible decision-making processes is particularly critical. The use of AI is often approved without a structured review. Documentation and responsibilities remain unclear. The result is governance that exists on paper but is hardly effective in day-to-day operations.
Reading tip: What is shadow AI in the company and how can it be detected?
The operational gap: Why AI policies are not enough
The widespread assumption that an AI directive is synonymous with Compliance, proves to be deceptive in practice. Guidelines merely describe a target state. Governance, on the other hand, means actually implementing this target state in the company and ensuring it on a permanent basis.
The real challenge therefore lies in operationalization. Many organizations have guidelines, but no consistent processes to ensure compliance. Decisions are made on an ad hoc basis, documentation remains incomplete and there is no systematic monitoring during ongoing operations.
In addition, responsibilities are often not clearly defined. Without clear responsibilities, a structural vacuum is created: risks are identified but not consistently addressed. At the same time, there is a lack of established mechanisms for responding to incidents in a structured manner.
The result is a gap between regulatory requirements and operational reality. This gap cannot be closed by additional guidelines, but only by establishing robust processes.
Reading tip: What is shadow AI in the company and how can it be detected?
Liability dimension of management when using AI
The AI Regulation is not aimed directly at individual members of management, but primarily at the respective responsible companies in their role as providers, operators or other market players of AI systems. Anyone who violates key provisions of the regulation, such as the prohibitions in Art. 5 or the operator obligations set out in Art. 26, must expect considerable consequences under supervisory law and fines.
For board members and managing directors, the risk is therefore primarily an indirect obligation to ensure proper corporate organization. Among other things, this includes clear responsibilities, a complete AI inventory, risk classification, approval processes, a Documentation and effective control mechanisms. If such structures are lacking, this may not only represent an operational deficit, but also a liability-relevant organizational deficiency.
If a lack of AI governance results in damage to the company, for example through fines, project terminations, reversals or cost-intensive remedial measures, the question of internal liability towards the company may arise. In Germany, liability under Section 130 of the German Administrative Offenses Act (OWiG) may also be considered if the necessary supervisory measures are not taken and this is precisely why the company is subject to fines.
The practical consequence is therefore that, although the AI Regulation does not create any completely new managerial liability, it does increase the requirements for proper organization and Compliance at management level. AI governance is therefore not an optional innovation topic, but a question of entrepreneurial diligence.
Governance as a workflow: a pragmatic approach
In order to close the operational gap, a rethink is required: away from static sets of rules and towards process-based governance. It is crucial that the handling of AI is transferred into a clearly structured process.
The first step is to systematically record all AI applications. This forms the basis for a consistent assessment that takes into account both regulatory requirements and operational risks. Decisions on deployment are no longer made informally, but follow defined criteria and are documented.
These systems must be continuously monitored during operation. Quality, risks and anomalies must be checked regularly. It must also be ensured that clearly defined response mechanisms are in place in the event of problems.
Such a workflow maps the entire life cycle of AI - from introduction to decommissioning. It translates regulatory requirements into concrete operational processes and thus creates the prerequisites for comprehensible and robust governance.
Ailance AI governance as a solution for companies
The introduction of effective AI governance rarely fails due to a lack of will, but rather due to implementation. This is precisely where Ailance AI governance comes in.
Ailance AI Governance translates the regulatory requirements of the AI Regulation into concrete, executable workflows. Instead of isolated guidelines, this creates a consistent system of inventory, classification, approval and monitoring.
Companies therefore not only receive Transparency about their AI landscape, but also the ability to actively manage and mitigate risks. Compliance in everyday life.
The focus is deliberately on practice: there are clear responsibilities, structured processes and reliable evidence.
Ailance turns governance into a functioning operating system for AI.
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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