Regulatory-to-procedure engine
GDPR articles, DSK/EDPB guidance and internal policies become concrete work instructions for business units.
- Roles and responsibilities
- Step-by-step instruction
- Audit and approval checklist
AI Compliance Operations
Turning laws into work instructions.
We translate GDPR, supervisory guidance, AI Act obligations and internal policies into auditable workflows: checklists, DPIA drafts, TOM measures, training notes and approval processes.
GDPR
AI Act
DPIA
AI usage policy
Positioning
Compliance rarely fails at the legal text. It fails at day-to-day implementation.
Data protection officers and compliance leads have to read legal sources, assess them, explain them internally, derive measures and maintain evidence. That is exactly where AI is strong, as long as sources, context and approvals are cleanly controlled.
AI Compliance Operations is not a legal-advice machine. It is an operational assistance system for auditable drafts and recurring compliance work.
Sensible cases
Where time is lost right away
AI Act deployer check
Companies review which AI systems they use, what role they hold and which obligations follow from that.
- Provider/deployer role check
- Human-oversight tasks
- Logging and information duties
DPIA preparation
The AI does not prepare a final data protection impact assessment; it structures questions, risks, TOMs and missing information.
- Questionnaire for the business unit
- Risk hypotheses
- TOM and evidence draft
AI usage policy
Company context, data protection rules and AI Act requirements become an understandable AI policy for employees.
- permitted and prohibited use
- prompt rules for sensitive data
- approval process for new tools
Processor and tool check
New SaaS and AI tools are pre-structured from available information: data types, provider, legal basis, risks.
- DPA checklist
- Data flow and storage location
- open questions for the provider
Training and awareness generator
Policies and real internal cases become short training modules, quiz questions and department instructions.
- Micro-learnings
- Role-based guidance
- Versioning on changes
Workflow
Sources in. Context added. Draft out. Human signs off.
The workflow is deliberately traceable. Every instruction stays traceable back to sources, company context and approval status.
official sources and internal policies
company context and process goal
AI draft with uncertainties
DPO/legal sign-off
versioned instruction and evidence
Entry point
Compliance Process Diagnostic
We take one concrete compliance process and review whether it can become a safe AI workflow. Not a large programme, but a solid scope for a pilot.
- define one process and the responsible owners
- collect sources, policies and examples
- structure obligations, case classes and risks
- test draft quality with AI
- define pilot scope and guardrails
Story ideas for SEO and content
Content that matches real search intent
From the AI Act to a work instruction
Story: a company already uses AI tools, but nobody knows who approves, documents and monitors. The obligations become an internal approval process.
A DPIA without an empty Word document
Story: the business unit wants to deploy a new tool. The AI produces questions, risk hypotheses and TOM drafts; the DPO reviews.
An AI policy employees actually understand
Story: instead of a long policy, short role-based rules emerge: sales, HR, support, management.
A tool check before the sprawl
Story: new AI tools are not banned but reviewed in a structured way: data, provider, purpose, risks, approval.
FAQ
Frequent questions
Does this replace the data protection officer?
No. The AI produces drafts, checklists and structures. Assessment, sign-off and responsibility stay with people.
Can laws be processed directly from the internet?
Yes, but only in a controlled way: sources must be approved, versioned and referenced traceably in the output. For productive workflows, official sources and internal approvals should be preferred.
What makes a good first pilot?
An AI Act deployer check or an AI usage policy is ideal, because the process is concrete, currently relevant and easy to scope.
Contact
One compliance process. One pilot. Clear guardrails.
The first step is a process diagnostic, before an AI workflow is built.