Move AI projects from pilot to production
without getting blocked by your Security team.

The problem is not a shortage of AI ideas. The problem is connecting a real use case to company data and taking it through the security review. Once the first project is approved, new use cases can use the same connections, permissions and controls instead of solving them again for every pilot.

AI becomes useful when it can work with real company knowledge. Without access to email, documents and databases, it gives generic answers. Connected directly, it can expose sensitive information, bypass existing access rules, create uncontrolled costs and leave no reliable record of what happened.

An independent control layer should sit between your teams, company systems and every AI provider. Installed inside the company’s infrastructure, it checks who is asking, which sources that person may access, what information must be removed and how much the request may cost before it reaches a model.

The controls should not belong to one model provider. The same connection works with Claude, Codex, ChatGPT, open-source and private models. Providers can be changed or combined without rebuilding permissions or data connections. Company knowledge is processed once, remains subject to existing access rules and can be used from any approved AI tool.

Every interaction leaves evidence. The company can see who accessed what, which policy was applied, what information reached the provider and what the request cost. This evidence supports internal governance, security reviews and obligations under the EU AI Act.

The monthly report makes usage and value visible. Leadership receives one view across every AI provider. Security can review access and evidence. Finance can understand spending and value. The teams can continue using the tools that work best for them.