Research, red-teaming and frontier experiments
Experiments ahead
of the rulebook.
Ready when you are.
Adversarial testing, prototypes and proofs of concept, framework evaluation and applied research — published where the work is ready to share.
Enterprise AI Lab
Where we try what’s next — before you have to decide about it.
The Enterprise AI Lab is EAIC's research division. We use it to stay ahead of the threat and compliance landscape — running adversarial testing on AI systems, evaluating emerging governance frameworks before they become mandatory, and publishing our findings where the work is ready to share.
Current work includes red-teaming methodology for high-risk AI systems under Article 9 of the EU AI Act, evaluation of ISO 42001 certification pathways for mid-market regulated firms, and developing a risk-adjusted ROI framework for agentic AI systems that sits outside existing governance models.
Research outputs are published here and via our Thought Leadership channel. Organisations can commission the Lab directly for applied research and red-teaming engagements. The build programmes themselves live on the members’ floor — see the dossier wall.
How the Lab feeds the firm
Everything starts as a question we cannot answer.
Work enters the Lab as a question, gets tested until there is a defensible answer, and then either opens a sector, goes into something we sell, or stays open. Most of it stays open, and that is the point — a lab whose every experiment succeeded was not running experiments.
This is what the dossier wall below actually is: not a portfolio, a pipeline.
| Dossier | The question | Where it points | Status |
|---|---|---|---|
| Carbon Ledger | Can a carbon claim be recorded so it survives challenge years later? | Carbon & waste | opened this sector |
| Evidence Graph | What if governance evidence were a graph instead of a filing cabinet? | Financial systems | still open |
| Fingerprint | What would an outsider find out about you before you noticed? | Financial systems | still open |
| Panopticon | How much can an environment know about itself without knowing who you are? | Next — spatial | still open |
| Rentarobot | Should hiring a robot be as easy as hiring a van? | Next — robotics | still open |
| Arbitrage Engine | Can six agents make decisions at machine speed, with someone still answerable for them? | Next — agentic control | still open |
“Where it points” is the sector a dossier is relevant to, not a claim that it has shipped there. One has opened a sector so far. The rest are open questions, and we would rather say which is which.
What the Lab is for
Our ambition has three parts. This is the first one.
We want to be a global leader in the understanding and real-world deployment of safe, responsible AI — and in proving the value it returns. Each of those rests on something different.
Understanding
The Lab
Evaluations, red-teaming, prototypes and published research. The half of the ambition that cannot be bought in.
Real-world deployment
The sectors
Citadel in production in regulated financial services, and working systems in local businesses that will never have a transformation budget.
Proving the value
Money, not scores
Risk expressed in pounds and opportunity in payback, so a finance director can argue with the number instead of admiring the dashboard.
How the Lab works
What is on the bench, and what we publish about it.
Each of these is published openly and available as a commissioned engagement. The method is free; the work is not.
Red-teaming
Attacking a system on purpose, before someone else does it for free. Adversarial testing frameworks for high-risk AI systems under EU AI Act Article 9 obligations, establishing what “adequate testing” means in practice. The Lab publishes what it finds; the bought version is a paid engagement.
The Inception Hack →The Article 9 method →
Active
Agentic AI governance
Developing governance and oversight models for autonomous AI agents that operate outside the human-in-the-loop frameworks current regulations assume. Risk-adjusted ROI modelling for agentic deployments.
Active
ISO 42001 certification pathways
Mapping ISO 42001 requirements against the Sentinel governance programme. Building an evidence bundle framework that supports certification without duplicating work already done in a Sentinel engagement.
Active
Shadow AI detection
Evaluation and improvement of AutoDiscover methodology. Testing detection rates across cloud platforms, API gateways and OAuth permission graphs. Publishing findings on shadow AI prevalence in regulated estates.
Active
Model evaluation
Evaluating whether a model does what someone claims, in the conditions it will actually meet. The Lab publishes the method; the bought version of this work is a paid engagement.
Published method
Robotics and spatial intelligence
The governance problems arrive before the robots do: who may operate one, who is accountable when it acts, what it is allowed to record, and what evidence survives afterwards.
Two open dossiers
The members’ floor
The dossier wall.
The Lab builds with client partners, not for spectators. These are the declassified covers of what’s on the bench — the working notes, prototypes and build programmes sit behind an engagement. More dossiers are declassified as projects leave private beta.
Dossier 01 — Robotics & spatial intelligence
RENTAROBOT
Hiring a robot should be as easy as hiring a van.
Booking, identity, insurance evidence, teleoperation and an operator marketplace for physical machines. The hard part isn’t mechanical — it’s who answers when nobody’s driving.
Read the dossier →Dossier 02 — Spatial intelligence — experimental
PANOPTICON
How much can an environment know about itself?
Total situational awareness for physical environments — splat-captured digital twins with change detection, governed hard enough to earn the name.
Read the dossier →Dossier 03 — AI & intelligence
ARBITRAGE ENGINE
Six agents. Machine-speed decisions. The governance problem of who answers for them.
Scout, Analyst, Executor, Guardian, Orchestrator, Historian — an autonomous trading architecture where Guardian answers the regulator and Historian answers the court.
Read the dossier →Dossier 04 — Circular systems
CARBON LEDGER
Carbon claims that survive scrutiny.
Every tonne traceable to a collection, a batch record and a process run. Carbon evidence built for audit, not marketing.
Read the dossier →Dossier 05 — AI & intelligence — core research
EVIDENCE GRAPH
Governance stops being a filing cabinet when evidence becomes a graph.
One graph across entities, systems, AI, assets, contracts, decisions and risk — so governance questions have answers instead of meetings.
Read the dossier →Dossier 06 — AI forensics
FINGERPRINT
What would an outsider find?
External attack-surface discovery and AI fingerprinting — detecting AI where nobody declared it.
Read the dossier →Working notes, prototypes and lab time are available to client partners under NDA.
Published research
Red-Teaming High-Risk AI Under Article 9: A Working Method
What EU AI Act Article 9 implies for adversarial testing — and the staged method the Lab uses, from threat modelling to evidence capture.
Read the research note →Governing Agentic AI: Questions Current Frameworks Don’t Answer
Why autonomous agents break human-in-the-loop assumptions — the concrete governance gaps, and the Lab’s working positions on interim controls.
Read the research note →The Inception Hack: Nested Prompt Injection in AI Second Brains
The most dangerous prompt is the one nobody wrote. How layered AI memory systems can be turned against their owners — and what Sentinel looks for.
Read the paper →Have a question the rulebook
hasn’t caught up with?
The Lab takes commissioned engagements — red-teaming high-risk systems, building experimental prototypes and proofs of concept, evaluating emerging frameworks, and applied research for organisations facing questions ahead of the rulebook.
Scoped per engagement · NDA standard · Lab access is for client partners