Security blocked your AI launch, and the feedback is a list of concerns instead of requirements you can build against.
An LLM now sits between your users and your data, and the controls you have were designed for neither.
You cannot answer the reviewer's basic questions: what can the model reach, under whose identity, and where is that logged?
Threat model the AI surface
Prompt injection, data exfiltration through tools, over-privileged service identities, poisoned retrieval: we rank the threats against your estate and launch plan, so effort goes where the risk is.
Map controls to the six pillars
Each threat gets a control, each control maps to a zero-trust pillar, and each control gets a policy test that proves it works. The map is the document your reviewers will actually read.
Implement, or specify for your platform team
We implement the controls in your stack, or hand your platform team an implementation plan with acceptance criteria. Both paths end with the policy tests passing.
Produce reviewer-grade evidence
Audit trails, incident paths, and the control map are written so a security reviewer can verify each claim directly instead of taking our word for it.
We already run zero trust on our network. Why is AI different?
The model is a new actor that reads your data, calls your tools, and can be manipulated by the content it processes. Network zero trust says nothing about what a model may do with a retrieved document. We extend the controls you already have to cover that actor.
Will this slow the product team down?
A system with legible controls clears security review faster, which is usually where the real delay lives. We design the guardrails with the product team so the safe path is also the convenient one.
Do you follow a specific framework?
We work from the Microsoft Zero Trust model across its six pillars because most reviewers already know it. Where your organization standardizes on something else, such as NIST 800-207, we map the same controls to that framework instead.