Security infrastructure for safe AI enablement.

Why it matters

A language model follows instructions, and it cannot reliably tell yours from an attacker's. Once it is connected to documents, tools, credentials or customers, a well-crafted input can turn a helpful assistant into a way in.

The weakness is rarely the model alone. It is the permissions, pipelines and trust boundaries built around it — which is where we look.

What we examine

• Prompt injection, direct and indirect — instructions smuggled in through user input, retrieved documents, web pages, emails and tool output.
• Agent and tool permissions — what each agent may call, with which credentials, and what stops a hijacked agent acting beyond its task.
• Data exposure — leakage of system prompts, retrieval and training data, secrets, and one customer's data into another's session.
• Retrieval and data pipelines — poisoning of knowledge bases, embeddings and fine-tuning sets.
• Output handling — model output passed unchecked into code execution, queries, browsers or downstream systems.
• Model and dependency supply chain — the provenance of weights, third-party models, plugins and libraries.
• Guardrails, logging and monitoring — whether misuse is detectable, attributable and stoppable.
• Identity, keys and limits — API key handling, authentication, and rate and spend ceilings.

What you receive

• A written report: every finding with its severity, the evidence, how to reproduce it, and how to fix it.
• A map of the system's trust boundaries, and where they are crossed.
• Remediation guidance in priority order — what to fix first, and why.

How an engagement runs

1. You submit a report describing the system, with any links and files that help us understand it.
2. We review it and email you a quotation and a time estimate.
3. Once you approve, the audit is carried out against the agreed scope.
4. You receive the findings and the guidance to act on them.