Agentic AI security
We build agentic systems with security designed in from the start. We help teams understand what their agents can do, reduce what can go wrong, and notice quickly when it does. No system is ever fully secure, but a good one is hard to misuse and quick to catch when it slips.
Autonomy, earned not assumed
So we treat it like one. Before you hand it that freedom, and the whole time it holds it, we ask three plain questions: what can it reach, what could it be tricked into doing, and what should it never be allowed to do?
We answer with a structured model of how agents fail, mapped to the frameworks the field is building around it: OWASP's work on LLM and agentic security, and MITRE ATLAS. So it holds up beyond our own judgement.
Securing agentic AI
Before an agent goes live, and while it runs, we map its attack surface, threat-model the autonomy, design guardrails and least-privilege identity, red-team it the way an attacker would, and put monitoring around it so misbehaviour shows. A background check doesn't end at the hire.
We build these systems ourselves, so we know where they tend to break. That is the difference between securing agents from the outside and securing them from the builder's seat.
Trust boundaries where an agent calls tools, reads untrusted data, and holds memory. Where the risk actually sits.
Tool allow-listing, least-privilege agent identity, prompt-injection defence, and policy-bound autonomy that holds up without wrecking the product.
Jailbreaks, indirect injection, exfiltration and tool-abuse chains, turned into a regression suite your CI can keep running.
Runtime visibility for when an agent drifts, and audit-ready evidence mapped to SOC 2, ISO 27001 and ISO 42001.
Built
The security work is grounded in a long habit of building and research: agents, harnesses, infrastructure, and experiments that tend to arrive early. A selection.
A personal AI assistant, and the harness it runs on. Built to improve itself over time, with guardrails from the start.
Watch and control your agents from any device, down to the Apple Watch, or from smart glasses while you work.
A private RAG pipeline that speeds up governance, risk, and audit work across your own documents.
Red-teaming AI models to find where they give up what they shouldn't, drawing on a background in offensive security (OSCP).
Automating admin work, and building image, audio, and text tools where the payoff is clear.
Frontier open models on your own hardware, local or cloud, tuned for the job, with nothing leaving your environment.
Sovereign stack
When the work calls for it, we build the whole column rather than a wrapper on someone else's API. Every layer is yours to inspect, host, and certify.
Security and governance is not a layer; it runs the height of the stack.
Multi-agent workflows, tool use, and policy-bound autonomy with full traceability.
Domain models tuned, aligned, and evaluated on your data, then owned, hosted, and kept.
Private inference, retrieval, and search: frontier open models served on your own hardware, air-gapped where you need it.
On-prem and sovereign-cloud compute, provisioned and hardened for accelerated workloads.
Track record
Our work spans fast-moving startups and heavily regulated enterprises: keeping the former quick on their feet with security that scales, and helping the latter modernise with cloud-native compliance that accelerates rather than obstructs.
Built security functions and architectures from the first hire up, with automated auditing and continuous monitoring proven across multiple certifications.
Research & direction
Agentic systems are shipping faster than anyone can test them. The question that matters is not how clever a model is, but how much damage it can do once it is wired into real tools.
Frontier labs answer that behind closed doors. We are working toward the opposite: a transparent, self-hostable way to measure an agent's blast radius, exercised against models we deliberately train to misbehave and keep in-house, so the answer is something you can run and check rather than a promise to trust.
Responsible research
Evaluation research, not offensive enablement. Adversarial models stay in controlled, isolated environments — never released as public weights, never pointed at live third-party systems — and the work follows responsible-disclosure norms. The line between measuring risk and creating it stays firm.
Latest
Malware is rewriting itself at runtime with LLMs. As models embed into software supply chains, understanding their internals becomes a security question. How do you run a background check on your AI agent?
Read more →Agent harnesses, guardrails, and evaluation tooling built in the open, including the scaffolding behind our own private agents. Yours to inspect, fork, and build upon.
View repositories →Get in touch
If you're building with agents, or weighing the risks, get in touch.