Health

AI Regulation Agent

Whether an AI system falls under the high-risk obligations hinges on its purpose. And then there is social data protection.

Determines when an in-house AI system falls under the high-risk obligations.

Open agent
Public administration

Four processes that take up the most time today

Developed in Germany · Hosted in the EU

Developed in Germany · Hosted in the EU or on-premises

Delivered together with

STACKIT Sovereign hosting in Germany, single-tenant from the first user.
Rödl Legal, tax and compliance expertise for legally sound deployment.
4P Consulting Advisory and implementation for the public sector, starting with the AI readiness assessment.

One project, two sets of rules, three units each with their own list of questions.

The business unit submits a project with a description that only outlines the purpose in broad terms. Compliance and data protection read the same description, classify it independently and arrive at different lists of questions. Until the classification is settled, the project goes back and forth between the units several times.

1projectThe business unit submits an AI system with a description of its purpose.
2sets of rulesOne asks about the risk class, the other about the legal basis and purpose limitation for the social data.
3rounds between the unitsCompliance, data protection and IT governance pass the project around until the purpose is described precisely enough.
1list of obligationsThe result is a list that has to be drawn up from scratch for the next project.

Once the purpose is classified against the same grid and the list of obligations derived from it, review effort falls by 50%.

The 50% reduction in review effort is this agent's headline figure. Two sets of rules and three coordination rounds describe a typical path to classification; they are not a measured value.

Three steps through the process.

The agent classifies the project by its purpose and derives the obligations with references; compliance and data protection sign off the approval.

Record the purpose and data types

The agent takes in the business unit's submission and breaks it down into purpose, affected persons and data types. It records anything that remains unclear as a question for the business unit.

Project sheet KI-2026-017, purpose in 4 sentences, 3 data types named

Determine the risk class with references

The agent goes through the criteria one by one and notes for each which detail from the project supports it. Criteria without a supporting detail stay open.

Classification against 6 criteria, 2 of them open for lack of detail

Prepare the documentation for approval

The agent compiles the classification, the derived obligations and the open points in a paper. It goes to compliance and data protection for signature.

einordnung_KI-2026-017.docx, 11 derived obligations, 2 open points

What the agent does not do.

It does not approve any AI system.

It presents the classification and the obligations with references. Compliance and data protection sign off the approval.

It does not replace the data protection assessment.

It names the data types in the project and the questions that follow from them. The data protection officer is responsible for the assessment.

It classifies nothing without a described purpose.

If the purpose stays vague, it records the affected criteria as open. IT governance puts the follow-up question to the business unit.

Connected systems

  • GRC tool
  • record of processing activities
  • document management

Operations

  • Deployment modelEU cloud, private cloud or on-premises
  • Data processingGDPR-compliant, no model training on your data
  • Responsible teamsCompliance, Data Protection, IT Governance

The questions that come up before the first meeting.

How quickly is the AI Regulation Agent ready to use?

A pilot typically runs on your own data within 4 to 6 weeks. The agent connects to your existing systems rather than running as a separate, parallel environment.

Where is the data processed?

EU cloud, private cloud or on-premises. Processing is GDPR-compliant and models are never trained on your data. Details in the Trust Center.

Where does the figure “50% less review effort” come from?

It is based on experience from comparable projects and process benchmarks, not a commitment. We work out the realistic effect for your organisation during the assessment before the pilot.

Related agents.

Intake review, case 2026/04711

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