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.
More and more AI use cases are processing personal information, from customer data and employee details to sensitive analyses. This automatically brings with it the obligations of GDPR in force: Every AI project with a personal reference must be Record of Processing Activities (VVT) and, depending on the risk, a Data Protection Impact Assessment (DSFA). Anyone who fails to plan ahead here risks data protection violations and project delays. The solution: AI governance and data protection—Compliance Hand in Hand. An integrated approach that includes an AI inventory, a Processing directory and seamlessly integrates DSFA processes. This fosters innovation and GDPR-Compliance not in the Contradiction, but in harmony.
Interface between AI inventory and processing directory
A central AI inventory of all applications is the starting point for good AI governance. This includes all of the company's AI systems and use cases with their purposes, data sources and responsible parties. At the same time, Article 30 GDPR a Processing directory (VVT or Record of Processing Activities, RoPA) for all processing of personal data. This is a structured register that shows who is processing which data, where, and for what purpose. These two areas (AI inventory and data protection register) should not exist separately from one another. Ideally, every AI—Processing containing personal information, directly with the Processing directory can be linked, for example, via a technical interface.
In practice, this means that when a department creates a new AI use case, the relevant information (e.g., purpose, data categories, storage location) is automatically recorded in the VVT or linked to an existing entry. This makes it clear at an early stage whether and what personal data an AI system uses and, if applicable, whether a Data Protection Impact Assessment (DSFA) is required.
This approach prevents gaps: No AI project operates „under the radar“ of data protection, and all AI-related processing activities are fully documented in the VVT. A well-maintained VVT is more than just bureaucracy; it serves as a management tool that helps identify risky data processing activities early on and then assess and address them.
Automated Data Protection Impact Assessment for AI Projects
Many AI applications are considered highly risky under data protection law, for example because they rely on Profiling, large amounts of data or new types of algorithms. The supervisory authorities emphasize that AI processing generally requires a Data Protection Impact Assessment (DSFA) are required, as they are often classified as high-risk activities. Rather than handling this requirement manually and late in the project, it should be automated and initiated early on as part of AI governance.
In concrete terms, this means As soon as the AI inventory indicates that a use case personal data If data is processed or certain risk criteria are met, the DSFA process is automatically initiated. Modern AI management tools such as „Ailance AI governance“integrate this step directly into the workflow: Only once the required Data Protection Impact Assessment Once this process has been carried out and documented, an AI use case can be fully approved. Risk-based workflows ensure that different checks are performed depending on the sensitivity of the data. If a use case contains personal data, the system automatically initiates the DSFA; in high-risk cases, it even includes additional verification steps.
This automation reduces time-consuming manual work and ensures that reliable documentation is generated for audits. In addition to the Compliance-In addition to security, the automatic DSFA trigger also delivers efficiency gains: Companies that have digitized their data protection impact assessments report a 60 to 80 percent increase in processing speed and a significant increase in the number of cases covered per Data protection team. It is important that the data protection officer remains involved, for example by being consulted by the system with every new DPIA and releasing the results. In this way Data protection Implemented by design: No AI system goes live without first assessing the risks and implementing appropriate safeguards.
Synergies: AI governance as a data protection enabler
A close integration of AI governance and data protection—Compliance creates enormous synergies. As a result, AI governance becomes an enabler for data protection—and vice versa. For one thing, existing data protection processes are integrated into AI workflows, so they no longer need to run in parallel. A good governance solution ties in with existing processes, such as the DSFA procedure and the Processing directory, and reflects the same roles and responsibilities as the others Compliance-structures. On the other hand, AI governance gains greater depth from a data protection perspective: Even during the planning phase of an AI project, principles such as Data minimization, Earmarking and access restriction are taken into account. The result is AI systems that are developed with data protection built in from the outset. Privacy by design is anchored technically and organizationally.
At the same time, data protection officers and Compliance-Responsible persons ensuring that AI projects are transparently recorded in the inventory and supported by meaningful documentation (e.g., model maps). Instead of laboriously piecing together information, they can obtain details at the click of a button about which data a model uses, for what purpose, and what risks have been identified in the process. Monitoring is made easier: Dashboards in the AI governance platform can, for example, show which use cases a Data Protection Impact Assessment which are considered critical or where reviews are pending.
This creates cross-divisional Transparency and prevents data protection from only taking place in separate silos. All in all, this creates a comprehensive governance approach that ensures the use of AI and the obligations under the GDPR at the same time. Companies can therefore drive forward innovative AI solutions without compromising data protection and Compliance to lose sight of it. Data protection is thus transformed from a brake on innovation into a co-creator: integrated AI governance increases the trust of users and supervisory authorities and reduces the risk of unpleasant surprises.
Practical Tips for Integrating AI and Data Protection Governance
Combining AI governance and data protection pays off. But how can this be implemented in practice? To wrap up, here are a few tips on how you can integrate data protection and AI governance from both a technical and procedural perspective:
- Maintain a central AI registry: Create a company-wide inventory of all AI applications. For each AI use case, you should specify the purpose, data types, Responsible persons and risk level must be documented. This register serves as the basis for all further Compliance-Steps.
- Ensure VVT integration: Link the AI inventory to your Record of Processing Activities (RPA). New AI projects that personal data should automatically be sent to the VVT. This is how you meet the GDPR-Documentation requirements and identify early on when additional tests are necessary.
- Automate DSFA workflow: Define rules for when a Data Protection Impact Assessment (e.g., for certain data categories or a high risk rating). Use tools or scripts that automatically initiate this process and monitor its progress. Involve the Data Protection Officer in the approval process to ensure a professional assessment.
- Define joint responsibilities: Establish clear roles for AI projects in which the legal/Compliance-The team and the IT/AI team work together. For example, an AI project cannot go live until both the technical Responsible persons and the data protection officer have both given the green light. Such dual approvals, which are logged in the system, increase reliability and acceptance.
- Ongoing review and training: Integration also means staying vigilant during ongoing operations. Set up regular reviews or re-audits to reassess AI applications and their data protection measures. Reminder features in the governance tool can automatically trigger these reviews. Additionally, train project managers and developers to consider data protection requirements from the very beginning—while the tool provides significant support, it does not replace a fundamental understanding of these requirements.
These measures make data protection and AI governance one: Compliance by Design becomes part of everyday life, and your company can take advantage of the opportunities offered by artificial intelligence without coming into conflict with the GDPR to get into trouble. Those who combine data protection and AI governance today will create the basis for trustworthy AI systems and long-term corporate success.
Experience for yourself how integrated governance works
With Ailance AI Governance, you can consolidate VVT, DSFA, and AI inventory into a single workflow—automated, traceable, and scalable.
Whether data protection officers, IT-Responsible persons or project manager: Everyone can see at a glance the status of approvals, which risks have been assessed, and which use cases still need to be reviewed.
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 learn more about him on his Author profile page.
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