Security that covers your models as well as your network.
Conventional cloud and application security, plus the newer surface: prompt injection, over-permissioned agents, data leaking through a retrieval index. If we build AI for you, this practice reviews it before it goes live.
Technologies we work with
Capabilities
Risk & compliance
Gap assessment against the framework you are held to, a prioritised remediation plan, and the evidence pack auditors ask for.
Identity & access
Single sign-on, privileged access, joiner-mover-leaver hygiene, and least privilege that survives contact with delivery teams.
Cloud security posture
Continuous configuration review across AWS, Google Cloud and Azure, with guardrails written into the infrastructure code.
AI & agent risk
Threat modelling for model-backed systems: injection, tool abuse, retrieval leakage, output handling and agent permission scope.
Data protection
Classification, encryption, masking and residency controls, including what may and may not reach a third-party model.
Detection & response
Logging and monitoring worth having, tested runbooks, and a rehearsed incident process rather than a document nobody has read.
What lands on your desk
Every engagement produces artefacts you can hand to a board, an auditor or a client.
Related practices
Already running AI in production?
We will threat-model it against the way these systems actually get abused, and tell you what to fix first.
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