Ship AI faster than your competitors, and still be able to explain it
Every department wants AI, and most of them have already started. Boards have stopped asking whether to allow it. They ask what it can reach, what it could say, and who would know if something went wrong. We build the guardrails that let AI accelerate instead of stall, and we do it without slowing the teams already building.
The gap between what AI can do and what anyone approved
AI arrived in most organisations from the bottom up. A team trialled an assistant, a developer wired a model into a workflow, a vendor shipped a feature nobody asked for, and none of it went through architecture review because none of it looked like a system. The result is a capability nobody can fully enumerate: tools in use that were never approved, assistants inheriting the permissions of whoever opened them, models downloaded from public repositories and run without anyone reading what was inside, agents given credentials because that was the quickest way to make a demo work. The people who built it were doing their jobs. What is missing is the guardrail layer, and the visibility to know where it is needed. The blind spots grow for as long as nobody is looking.
Guardrails that let AI move fast, safely.
Generative AI initiatives were scaling quickly across the organisation, every department wanted in, but the guardrails had not kept pace: no clear guidance on approved tools and systems, limited internal AI-security depth, and no structured way to see or manage the risks the new systems introduced. The business needed to keep moving at the speed the market expected, with a level of safety and visibility it did not yet have.
The organisation gained a level of AI safety and visibility it had not had before, and used it to accelerate rather than brake: AI use and functionality increased across the business on top of a governance foundation that made each deployment defensible. Vulnerabilities found in red-teaming were remediated before they became incidents, and secure deployment is now the default path rather than the exception.
Read the engagementWhat usually brings people to this page
Most AI security work starts with one of these. If two are true, it is already overdue.
AI is scaling faster than governance
Every function wants in, delivery is moving, and nobody can say which tools are approved or what the deployments can reach.
The board asked what could go wrong
A question that needs a structured answer about risk, controls and detection, not a reassurance from the team that built it.
Shadow AI is obvious but unmeasured
People are pasting work into consumer tools because the sanctioned path is slower. Banning it has not worked.
Agents have been given real credentials
An agent that can act on systems is a new kind of privileged account, and most were provisioned without being treated as one.
You run models you did not build
Open and third-party models arrive as files that can carry code, and few teams inspect them the way they would inspect any other download.
A framework now applies to you
ISO 42001, the NIST AI RMF, the EU AI Act or a regulator's expectations have moved from guidance to something you must evidence.
Six things have to be true, in this order
1. Inventory
- Every model, assistant, agent and embedded vendor feature actually in use, sanctioned or not
- What each one can reach: data, systems, and the permissions it inherited from whoever runs it
- Ownership recorded, because an AI system with no owner cannot be governed or retired
2. Supply chain
- Third-party and open models scanned before they run, for hidden code, tampering and vulnerable dependencies
- Provenance recorded for every model, dataset and library your applications depend on
- The same discipline applied to AI features switched on inside software you already own
3. Risk assessment
- Use cases assessed against a recognised framework rather than a general sense of unease
- The failure modes that matter for each: leakage, prompt injection, unsafe action, hallucination relied on as fact, biased or harmful output
- Severity and likelihood recorded so the work can be prioritised and funded
4. Guardrails
- A sanctioned path to production that is genuinely faster than going around it
- Controls at the boundary: what a model may read, what it may say, what it may act on, and what must be reviewed by a person
- Policy and regulation translated into controls that produce their own evidence, so compliance is a by-product rather than a second project
5. Testing
- Red-teaming against the systems you are about to rely on, before they matter
- Prompt injection, jailbreaks, data exfiltration, multi-turn manipulation and tool misuse, tested rather than assumed
- Findings remediated and retested, and the tests kept running as the models and the attacks change
6. Detection and response
- Your security operations able to see AI activity, including what agents do, as signal rather than noise they have no context for
- Playbooks for the incidents this technology actually produces, rehearsed before the first one
- Reporting a board, an auditor or a client will accept as evidence of control
Where we look
What your people use
- Assistants embedded in the productivity suite, and what they can reach on each person's behalf
- Consumer AI tools reached from work devices and accounts, the shadow layer
- AI features switched on inside software you already bought, often without an announcement
What your teams build
- Models and applications your engineers have put into production
- Agents with credentials, integrations and the ability to act on real systems
- The data pipelines feeding all of it, where most of the real exposure sits
What you brought in
- Open and third-party models, and the files, weights and dependencies they arrived with
- The training and fine-tuning data behind them, and whether it can be trusted
- Model registries and hosting platforms, where a tampered artefact becomes everyone's problem
What it connects to
- The tools, APIs and connectors an assistant or agent is allowed to call, which set the real blast radius
- The identities, keys and service accounts AI runs under, and whether anyone could revoke them in a hurry
- The logs and monitoring that would show what an agent did, or reveal that nothing was recording it
Security is what lets you go faster here
The instinct when AI risk surfaces is to slow down, and it is the wrong one: the teams route around the control and you lose the visibility as well as the time. The organisations getting value from AI are the ones that made the safe path the quick path. A sanctioned route to production that takes days beats a policy that takes weeks and gets ignored. Guardrails that let a team ship with confidence produce more AI in production, not less. We have watched this run both ways, and the organisations that moved fastest had built the sanctioned path first, whatever their appetite for risk.
From a capability nobody can enumerate to one you can defend
Before, the honest answer to what AI is in use here is a list someone assembled by asking around, and the honest answer to what it can reach is a shrug. After, there is an inventory with owners, models you can vouch for, an assessed risk register, a sanctioned path teams actually prefer, and a security function that can see AI activity and act on it. That foundation is what makes the next deployment defensible.
You already have a security function. Why is AI different?
Because the failure modes are unfamiliar and the usual controls do not see them. A model does not get breached in the way a server does; it gets persuaded. Content fetched from a web page or a document can carry instructions your system follows as though a colleague had typed them, and a patient attacker can get there one innocuous message at a time. A model file downloaded from a public repository can carry code that runs the moment it is loaded, and the application-security scanners you own were not built to read it. An agent with legitimate credentials can take an action nobody authorised without bypassing a single control. And some of the damage is not an attack at all: a confident answer that is wrong, relied on by someone who had no way to tell, or output that is biased, harmful or not what your brand would say. The most likely first incident is plainer than any of that: data the assistant could always reach, surfaced to somebody who was never meant to see it. What your security team lacks is inventory, AI-specific threat context and playbooks, and that is what this work supplies.
Choosing tooling, without taking anyone's word for it
We are independent of any one platform, so here is the test we apply on your behalf, and which you should apply to us. Does it see the AI actually in use, including the tools nobody registered, or only what you tell it about. Does it inspect the models you bring in before they run, or trust the repository they came from. Does it inspect what a model can reach rather than only what it was configured with. Can it enforce at the boundary, on inputs, outputs and the actions an agent takes, without a rewrite of every application. Does it test the failure modes that matter for language models and agents, adapting the attacks to your use case, or reuse an application-security checklist with a new label. Does it turn your policies and the frameworks you answer to into controls and evidence, so the governance work is a by-product rather than a second project. And what does it cost your delivery teams in friction, because a guardrail that slows a sprint will be gone within a quarter. If a vendor cannot answer those plainly against your own systems in a demo, that is the answer.
See it, measure it, or prove it
Demo
A working walkthrough against a scenario like yours, run by the engineer who would deploy it, not a sales team.
- The capability shown end to end on realistic data
- Your questions answered by the people who implement it
- A plain view of what it would take to run in your environment
AI Risk Assessment
The size of the exposure, evidenced: where AI is in use, what it can reach, and which guardrails are missing.
- Maturity rating against a recognised framework
- Risk register with severity and likelihood
- Policy and control gap assessment
- Prioritised remediation plan
- Board-ready summary of exposure
Proof of value
Test the case before you scale it: one use case, one sponsor, a controlled environment and a fixed budget.
- Working pilot in a controlled environment
- Measured results against agreed success criteria
- Data, risk and governance findings
- Scale-up plan and cost estimate
- A scale, adapt or stop recommendation
Start with a conversation
Choose how you want to begin. A partner replies within one business day.
Before you enquire
Almost certainly not: the inventory step usually finds AI already in use through embedded vendor features and consumer tools on work accounts. Starting before your own first deployment is the cheapest time to do it, because the guardrails exist before there is anything to retrofit.
No. We are independent of any one product, and this page names none. Where tooling helps we bring what suits your environment, show it working against a scenario like yours in the demo, and leave you free to license it yourself, through us, or not at all.
The opposite is the intent, and it is the measure we would hold ourselves to. A sanctioned path that is slower than going around it has failed. We design the route to production first, then the controls that sit on it.
A maturity rating against a recognised framework, a risk register with severity and likelihood, a policy and control gap assessment, a prioritised remediation plan, and a board-ready summary of exposure. It is scoped to evidence the size of the problem and to say what to fix first.
They are the frameworks we assess against and the shape the evidence takes. We work to them because they are what a board, an auditor or a client will recognise, not as a certification exercise in their own right. Where a guardrail can be written to produce the evidence a framework asks for, we write it that way, so the compliance evidence accumulates as the controls run.
Yes, though the emphasis shifts. Hosted models from a major provider carry a different risk from a file downloaded off a public repository, but the features your vendors switch on, the libraries and agents your teams pull in, and the data used to fine-tune anything are all part of the same chain. The step is about knowing what you depend on and being able to vouch for it, whoever made it.
They overlap and they are not the same. Most AI incidents begin as data exposure, because an assistant inherits the reach of the person using it. If the data estate has never been mapped, start there and we will say so; if it has, this work builds on it.
The same senior people throughout. A partner stays accountable from the first conversation to the outcome, alongside the engineers doing the build. Afterwards you can run the operating model yourself, or we can run it with you under a managed arrangement with agreed service levels and reporting.