What we do
Microsoft Foundry
Landing zone, governance and cost control for AI workloads, so a pilot reaches production through the same security review as any other workload.
- Frameworks
- CAF: Govern, Secure, Manage · Well-Architected: Security, Cost Optimisation
- Good first step
- Microsoft CAF Readiness Assessment, a baseline in 3–4 weeks
The problem
AI pilots usually start outside the platform. A team creates a project, points it at some data, and it works. Then someone asks which data left the tenant, who approved the model, how the content filters are set and what it costs each month. Those questions have no owner yet, so the pilot stalls at the point it was meant to scale.
You probably need this if
- A working AI pilot can't get sign-off for production, and the reason is hard to pin down
- Model and agent deployments happen team by team, so there's no single list of them
- Content safety policy is still at its default settings
- It's unclear which data the model was grounded on, or who approved it
What the service covers
- 1AI landing zone: Foundry project and resource structure, private endpoints, and public network access disabled by default
- 2Model governance through the Foundry control plane: which Foundry Models are approved, at what version, with content safety policy set deliberately for each
- 3Agent governance: Foundry Agent Service deployments managed centrally, so every agent in production has been signed off
- 4Identity and RBAC across projects and connections, so access to a model and access to its grounding data are granted separately
- 5Token limits and cost attribution per project and per model, so AI spend is metered from the first month
- 6Observability and evaluation wired into your existing operations: tracing, agent evaluation, and AI red teaming before a workload faces the public
- 7Governance mapped to the department's AI assurance obligations, including the Algorithmic Transparency Recording Standard, answered from the platform's own records
Other areas we work in
All 8 areasAzure Platform Architecture
We design the Azure platform, check that it holds under its own governance, and hand it to your team with the controls already deployed.
Cloud Security Architecture
We design the detection, tune it to your estate, and hand your team a backlog they can work through.
Governance & Compliance
We set up Cloud Adoption Framework governance as working controls in Azure, with the evidence to show an auditor when they ask.
Getting a pilot to production
We start from the pilots you have running and what your assurance process asks for, and map what needs to be in place before one can scale. If agents are already deployed, the Agentic AI Security Assessment takes two to three weeks and gives your assurance panel a basis for sign-off.