CEO
Can the company move quickly without creating uncontrolled data or decision risk?
AI security and GDPR posture
AI systems should be useful without losing control of company data.
Ferre Torres B.V. helps companies design AI-first systems with explicit data boundaries, access control, review paths, logging, evaluation, and hosting choices before RAG, agents, dashboards, or Company Brain systems scale.
- Data boundaries
- Role-based access
- Audit trails
- Human review
Security-first scoping
Define the control model before the AI workflow grows.
- Clarify which data categories, documents, metrics, users, and tools are in scope for the first build.
- Decide where the system runs: client cloud, private tenant, approved SaaS, or isolated prototype environment.
- Preserve source-system permissions when building RAG, dashboards, agent workflows, and internal assistants.
- Design logging, monitoring, evaluation, and human review around the workflow's real business risk.
Common control areas
The architecture questions that reduce enterprise AI risk.
Data minimization
Only connect the sources and fields needed to prove the first workflow.
Access control
Keep user, group, document, dashboard, and agent permissions explicit.
Evaluation and monitoring
Track retrieval quality, grounded answers, failures, cost, latency, and usage.
Approval gates
Require human review where decisions, client communication, money, or compliance risk matter.
Buyer questions
What CEOs, CTOs, legal, and compliance teams should ask early.
CTO
Where will data flow, how are permissions enforced, and who owns production operation?
Legal and compliance
Which data categories, retention expectations, review points, and vendor constraints apply?
Users
What can the assistant see, what should it refuse, and when should it escalate?
AI security questions
Security and GDPR questions before implementation.
Can enterprise AI systems be built without exposing company data publicly?
Yes. Enterprise AI systems can be scoped around private environments, controlled data sources, role-based access, approved model providers, and clear restrictions on what data is sent to external services.
What should a GDPR-aware AI project define first?
A GDPR-aware AI project should define data categories, purpose, legal and compliance ownership, access boundaries, hosting choices, retention expectations, audit needs, and human review points before implementation.
How should RAG permissions be handled?
RAG permissions should preserve the access rules of the source systems so users and assistants only retrieve documents, metrics, or records they are allowed to see.
Is this legal advice?
No. This is technical architecture guidance. Legal and regulatory decisions should be reviewed by the client's legal or compliance advisors.
Security-aware AI next step
Bring the workflow, data sources, and constraints before the first MVP.
Share the systems, data categories, user groups, hosting preferences, approval requirements, and business result the AI workflow should prove.