AI is already
governing your
operations.
Who governs AI?
AI is making decisions in enterprise systems right now — recommending, routing, flagging, executing. Without a governance architecture, there are no defined boundaries, no cost controls, no audit trail. We design the model. Arbiter enforces it.
2
Service Tracks
5
Architecture Outputs
Arbiter
Enforcement Layer
The questions your board will ask
“Which AI decisions are being made autonomously — and who approved that?”
Most organisations have no defined answer. That’s the governance gap.
“When AI spend drifted last quarter, who caught it — and how long did it take?”
Cost without attribution isn’t a budget problem. It’s a governance problem.
“If compliance asked which model made a specific decision last month, could you answer?”
Without an audit trail, the answer is almost always no.
“What happens if an AI output causes an operational error — who owns it?”
Accountability vacuums don’t show up until something goes wrong.
The Governance Gap
Three problems every organisation ignores — until they can’t.
These aren’t future risks. They’re present conditions in most enterprises deploying AI today. The organisations that address them proactively don’t do so because they’re more cautious — they do so because they’ve seen what happens when they don’t.
01
No Decision Boundaries
AI is making operational decisions without defined approval rules. Who decides what AI can act on autonomously? What requires human review? At what threshold does an AI recommendation become an AI action? Most organisations have no answer to any of these questions.
“We didn't realise the model was routing orders automatically until a customer complained.”
02
No Cost Controls
Every AI call has a cost. Teams experiment independently. Models are called unnecessarily. Premium models get used where cheaper ones would suffice. Spend drifts without attribution — and by the time someone notices, months of waste have already accumulated with no way to trace it to a team, project, or decision.
“The API bill doubled in three months. We still can't tell you exactly why.”
03
No Audit Trail
Compliance will eventually ask which model made a specific decision, who approved it, what it cost, and what guardrails were in place. Regulators are building frameworks that assume organisations can answer these questions. Most organisations today have no way to answer any of them — because no one designed for auditability from the start.
“We couldn't demonstrate to our auditor which AI outputs had been reviewed.”
Service Tracks
Two engagements.
One governance model.
The right starting point depends on where you are. AI Governance Audit is for organisations that need to understand their current exposure first. Governance Architecture Design is for organisations ready to build the model Arbiter will enforce. Both tracks lead to the same destination.
Track 01 · Starting Point
AI Governance Audit
Best for
Organisations where AI is already operating across systems — but governance has not been formally defined. You need to understand your current footprint and exposure before you can design controls for it.
- ›Complete AI system and tool inventory across all teams
- ›Decision boundary mapping — what AI is acting on autonomously
- ›Cost visibility gaps and attribution analysis
- ›Accountability and ownership gaps identified by role
- ›Prioritised governance gap assessment with risk ranking
- ›Arbiter module activation sequence — which controls to deploy first
Track 02 · Full Architecture
Governance Architecture Design
Best for
Organisations scaling AI across operational or engineering systems — and ready to build the complete governance model that Arbiter will enforce at the infrastructure level across the full organisation.
- ›Unified decision authority model across all AI use cases
- ›System access and boundary rules by team, role, and environment
- ›Enforcement and change control framework
- ›Cost controls and budget accountability model
- ›Output guardrail definitions and escalation procedures
- ›Full Arbiter deployment architecture and rollout plan
The Architecture
Five outputs.
All Arbiter-ready.
Every governance architecture engagement produces five specific artefacts. Each is designed to be immediately deployable into Arbiter — not a document that sits on a shelf, but a set of rules the infrastructure can enforce from day one.
01
Decision Boundary Map
A complete map of every AI use case in your organisation — defining what each model is permitted to act on autonomously, what requires human review, and what is out of scope entirely. The foundation of every enforcement rule in Arbiter.
02
Approval Chain Definition
Documented approval chains for AI adoption, model changes, and policy exceptions — by team, role, and risk level. Defines who can authorise what, at what threshold, and what the escalation path looks like when something falls outside the boundary.
03
Cost Control Framework
Spend limits, attribution rules, and budget thresholds defined at the workspace, team, and user level. Designed to be deployed directly into Arbiter's Budget Controls module — so limits enforce automatically, not just report after the fact.
04
Enforcement Ruleset
A complete set of deterministic enforcement rules — model restrictions by team, user-level access controls, output guardrail definitions, and override procedures. Written in Arbiter's rule format, ready to activate immediately on deployment.
05
Audit Trail Design
The logging and attribution architecture that answers the questions compliance will ask — which model, which team, which user, what it cost, what guardrails were active, what was blocked. Designed for the audit requirements your organisation will face.
06
90-Day Arbiter Roadmap
A sequenced deployment plan for Arbiter — which modules to activate first, at what thresholds, in what order. Designed to start in observe mode across all modules and move to enforce progressively, with defined milestones at each stage.
The Enforcement Layer
The architecture
defines the rules.
Arbiter enforces them.
Governance architecture without enforcement is policy theatre. The rules need to be active at the infrastructure level — evaluated on every AI request, automatically, without relying on teams to self-govern.
That is exactly what Arbiter does. It sits between your applications and your AI providers. Every request passes through it. The rules your governance architecture defines become the enforcement logic Arbiter runs.
Governance Architecture defines
Arbiter module that enforces it
Decision boundaries — which AI actions require human approval vs can execute autonomously
Rules evaluated before every request. First match wins. Requests outside boundary blocked before the model is called.
Cost controls — spend limits at workspace, team, and user level
Monthly limits set per workspace and per user. Requests that would breach the limit are blocked before the model is ever called.
Audit requirements — which model, which team, what it cost, what guardrails were active
Every request logged from the first call. Provider, model, token counts, cost, latency, attribution by user and project.
Model restrictions — which teams can use which models, at what thresholds
Block by model, team, or user. Rules require no code changes in applications. Full audit log of every blocked request.
Output guardrails — dangerous content categories to intercept before delivery
Shell commands, SQL mutations, credential leaks, infrastructure actions intercepted pre-delivery. Observe → warn → enforce progression.
Anomaly detection — usage patterns that signal misuse or runaway spend
Post-request pattern monitoring. High-cost request detection, premium model overuse alerts, repeated expensive prompt detection.
Ariva AI defines the governance model. Arbiter enforces it.
One URL change. No other code modifications. Fail-open guaranteed — Arbiter failure never blocks requests.
Who We Work With
Organisations that want
discipline — not just speed.
AI governance isn’t for every organisation at every stage. The right moment is when AI is already influencing operational decisions — and the absence of a governance layer has moved from a future risk to a present condition.
Strong Fit
- ›AI is already operating across engineering or operational systems
- ›Deploying AI coding tools (Claude Code, Cursor) at team or organisation scale
- ›Concerned about decision accountability, cost control, and audit readiness
- ›Operating in regulated environments where AI governance is a requirement
- ›Scaling AI adoption and want it to remain controlled as it grows
- ›CTO or CISO has been asked to demonstrate AI governance to the board
Likely Not a Fit
- ›Looking for AI experimentation without operational governance
- ›Wanting a vendor to build and deploy AI tools on your behalf
- ›Not ready to define approval, ownership, and accountability for AI decisions
- ›AI is only in early pilot stages with no operational impact yet
- ›Looking for a compliance checklist rather than enforceable infrastructure
If AI will influence operational decisions in your organisation, governance is not optional — it is infrastructure.
Not Ready for a Full Engagement?
Start with the free
AI governance score.
Ten questions. Three output areas. An honest picture of where your AI governance is exposed — across Visibility, Governance, and Control. No account required. No sales call. Results delivered instantly, with gap-specific Arbiter recommendations.
- ›A 0–100 governance score across three dimensions
- ›Your specific visibility gaps — what AI is doing that you can't see
- ›Control recommendations mapped to exact Arbiter modules
- ›Takes under 10 minutes. No preparation needed.
Sample Question — Governance
Who in your organisation owns accountability when an AI decision causes an operational error?
Define the Rules
Before AI scales,
define who
governs it.
Start with the free governance score to understand your exposure — or request an engagement directly if you’re ready to build the architecture that gives AI a defined boundary to operate within.
Engagements are limited. We work with a small number of organisations at a time.
Also: AORA — Operational Resilience Assessment for apparel & supply chain →