Give your AI programme clear boundaries and accountability.
Define what AI may do, which data it may use, who is responsible and what evidence is required before deployment.
Review your AI controlsGovernance that connects policy to practice.
We help translate business expectations into an AI inventory, use-case risk assessments, approval paths and operational controls. The aim is a process that business, technology and security teams can use throughout the lifecycle.
Security around data, tools and actions.
Design work can include identity and access controls, secrets handling, network boundaries, logging, retention requirements and provider review. AI-specific assessments consider misleading inputs, prompt injection, unintended data disclosure and excessive tool permissions.
Know where processing happens.
We map where data is stored, processed, backed up and accessed, including third-party services and support contributors. Hosting location alone does not describe the complete data boundary. Country-specific requirements are reviewed with your legal, privacy and security stakeholders.
What you receive.
A risk and control register, data-flow map, ownership model, implementation priorities and evidence requirements. Specialist testing or independent assurance can be scoped separately where needed.
Practical controls, accurately described.
Controls can support an assurance programme, but a deployment architecture does not itself establish certification or legal compliance. Any assessment or attestation is described by its actual scope and issuing party.
Enterprise AI Model Governance on Azure
A custom Azure-based LLMOps platform developed for a private equity firm to organise model usage across engineering projects through a managed endpoint gateway, model registry and discoverable catalogue.
Explore the projectReview your AI controls
Tell us what you want to deploy or improve. We will discuss hosting, integration and operational requirements.
Review your AI controls