Move AI projects from pilot to production
without getting blocked by Security.
Connect AI to company data with the permissions, data protection and usage records required for approval.
The problem is not a shortage of AI ideas. The difficult part 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.
This is how AI should work inside a company.
Teams should be able to use the AI tools that fit their work and connect them to approved company data. The company should keep control of access, information and cost independently of the model provider.
An independent control layer should sit between your teams, company systems and every AI provider. It is installed inside the company’s infrastructure, so the company’s own rules remain in control while teams continue using Claude, Codex, ChatGPT or private models.
Teams + data
People keep using approved AI tools and the company systems they already have permission to access.
Email · documents · collaboration tools · databasesIndependent control layer
Every request follows the same controlled path before it reaches a model.
- Identity + permissions
- Sensitive-data protection
- Company policies
- Spending limits
- Evidence
AI models
Providers can change or be combined without rebuilding the company’s controls and data connections.
Claude · Codex · ChatGPT · private modelsThe company keeps control of its data and policies independently of the AI provider.
Every request should be controlled before it reaches a model. The layer checks identity and existing permissions, removes sensitive information when required, applies company policies and spending limits, and records what happened.
Company data should be connected once and used from any approved AI tool. Documents, messages and databases remain subject to their original access rules. Providers can be changed or combined without rebuilding every connection, permission or knowledge index.
Governance should be based on evidence. Each interaction records who made the request, which sources were accessed, which policy was applied, what reached the provider and what it cost. This evidence supports Security reviews, internal governance and the company’s obligations under the EU AI Act.
How it gets implemented
Implementation should not create another internal platform to build and maintain. The Agile Monkeys takes the service from the first use case through installation and ongoing operation.
- 01
The use case is understood.
The tools, data sources, users, existing permissions and Security requirements are defined before any installation begins.
- 02
A control plan is created.
The required connections, policies, sensitive-data rules, spending limits and evidence are agreed for the use case.
- 03
The layer is installed inside the company’s infrastructure.
Approved systems and AI providers are connected. The Agile Monkeys takes the implementation through the company’s own Security review.
- 04
The service is operated every month.
Connections, permissions, policies, usage and costs are monitored as teams, providers and regulations change. Governance evidence and operating reports remain available.
Give your security team everything they need to approve your AI project.
The Agile Monkeys is the team in the middle, currently implementing this service with a limited number of companies and institutions. Share your use case and any relevant requirements around company data, security or compliance. The team will review the context and get in touch directly if there is a clear way to help.