From principles to controls
Policies become concrete approval rules, technical guardrails, evaluation thresholds and evidence requirements.
Govern · AI governance & security
02 / 04Make responsible AI operable.
We translate policy, regulation and security principles into controls teams can apply without stopping useful experimentation.
Discuss your AI roadmapThe Numezis system · 02
How can teams use powerful AI safely, consistently and with evidence?
Governance succeeds when it makes the safe path clear and fast. Every control must have an owner, evidence and a reason to exist.
Read the case studyWhat changes
01
Controls embedded in delivery
02
Traceable risk acceptance
03
Safer use of sensitive data
04
Governance that scales with adoption
The work
Security by architecture
Identity, permissions, data boundaries, model behavior and supply-chain risk are addressed before production.
Proportionate assurance
Low-risk productivity use should not face the same process as a customer-facing or decision-support system.
Case study
From shadow AI to governed use at a Swiss financial services firm
How a regulated Swiss financial firm replaced shadow AI with a governed framework: inventory, risk tiers, controlled access and measured proof.
Read the case studyAI uses inventoried0 → 68
Uses covered by a proportionate control0% → 100%
Approval time for a new low-risk useno process → < 48 h
Anonymised case — representative example from real Numezis engagementsMake the next decision explicit
Start with the situation you actually have.
In a first working session, we frame the decision, the evidence required and the smallest credible next step.
Discuss your AI roadmap