Cybersecurity
Cybersecurity for the AI you are already running.
AI systems fail in ways your existing controls were never written for. We break them on purpose, then build the guardrails and the paper trail that survive a real review.
What a guardrail looks like
Watch one refuse.
A visitor asks the agent for another customer's data. This is what should happen, every time: a refusal, a log entry, and a person told about it.
100%
of actions logged
0
PII leaves your tenant
24/7
human-in-the-loop on risk
The refusal is the easy part. The part that matters in a review is everything around it: that the request was recorded, that the record cannot be edited, and that a named human saw it inside a defined window.
We build all three, and we test them by attacking the system until one of them breaks.
Where we usually get in
The model is not the weak point.
- An agent with more access than the person using it
- Prompt injection buried in a document or a web page
- Customer data leaving your tenant through a model provider
- Tool calls nobody logged, so nobody can reconstruct them
- A jailbreak that works and a support team that never hears about it
What we build
Defence you can hand to an auditor.
- Threat modelling while the architecture is still cheap to change
- Red-teaming your agents the way an attacker would
- Guardrails on input, output and every tool call
- Least-privilege identity for agents, not shared service accounts
- A complete audit trail, and alerts on the requests that matter
- An evidence pack mapped to the controls your auditor names
Have us try to break it first.
Point us at the agent you are least comfortable with. You will get the attacks that worked, the fixes in priority order, and the evidence that they hold.