Skip to content

Enable innovation with proportionate controls, transparent accountability and secure engineering.

Policy pressure, shadow AI and regulated use cases stall adoption when governance arrives late and heavy. We design proportionate controls into intake, build, release and operation, so responsible use enables delivery instead of blocking it.

Discuss this challengeScore an initiative first
Layered AI governance frameworkSix planes stacked vertically, from AI systems at the base up through visibility, risk signals, risk classification, control and accountability at the top. A policy pillar runs down the left side and connects into every layer.PolicyAI systemsVisibilityRisk SignalsRisk ClassificationControlAccountability

Governance

A Systematic AI Governance Framework

A systematic yet pragmatic governance framework, grounded in the relevant regulations and standards, that acts as a steering wheel rather than a brake.

Visibility
You cannot govern what you cannot see. Therefore visibility of the AI landscape is the first critical step in the process. An up to date inventory of all use cases, models, agents, and their relationships is the starting point.
Risk Signals
Risk Signals are the foundation of an effective governance system. Application of the governance policies, together with the visibility layer, produces risk signals that can be mitigated.
Risk Classification
Risk classification is where risk signals become risk tiers. Each use case is graded against the framework's criteria, so the depth of control matches the consequence.
Control
The control surface is where risk tiers become required controls. As a component of the governance framework, the control surface sets which controls and actions are required.
Accountability
Accountability is where required controls become owned actions. Each one maps to a named owner and a timeframe, so who acts, and by when, is never in question.
Evidence produced by the work
Evaluation results, approvals, model versions and decision logs should fall out of delivery as it happens. Assembling them afterwards costs more, takes longer and convinces fewer people.

Where the control has to sit to be worth anything

Governance fails when it is a gate bolted on at the end, because by then the expensive decisions are already made and the only options left are approve or waste the work. These are the four moments where a control genuinely changes the outcome, and what each one needs to be more than a sentence in a policy.

At intake, before anyone builds

The cheapest moment in the entire lifecycle. A decision here costs a conversation. The same decision after launch costs the build, the rework and the credibility of whoever approved it.

What that takes

  • A single front door for new AI ideas that people know about
  • Purpose, data and affected people stated before scoring
  • A risk tier assigned by criteria rather than by debate
  • A named decision-maker per tier, with a published turnaround
  • A register entry created whether the answer is yes or no
  • A fast lane for the low-risk majority

During the build, while change is cheap

Design decisions quietly become compliance facts here. What the system may read, what it may do, and where a human stands are all far easier to set now than to retrofit later.

What that takes

  • Lawful basis and data minimisation settled before the first pipeline
  • A threat model covering injection, leakage and over-broad agent authority
  • Human checkpoints designed in where consequence sits
  • Test cases for harmful, adversarial and edge inputs
  • Access scoped per environment, with production kept separate
  • Decisions recorded as they are taken, not reconstructed later

At release, as a criterion not a ceremony

A sign-off meeting where nobody can say what good looks like is theatre. Thresholds agreed before the results exist turn the same meeting into a decision.

What that takes

  • Quality, safety and bias thresholds agreed before results are seen
  • Evaluation run on a fixed set, so releases can be compared
  • Results tied to a specific model and prompt version
  • Sign-off from somebody accountable for the outcome, not the project
  • Rollback tested rather than assumed to exist
  • Users told plainly what the system can and cannot do

In operation, where reality leaves the test set

Live use is broader, stranger and more adversarial than anything you tested. Systems degrade quietly, and the people who notice first are usually your customers.

What that takes

  • Output sampled and reviewed on a schedule somebody owns
  • Drift detected in the inputs and in the behaviour
  • A route for users to flag bad output that reaches a human
  • Cost and usage watched alongside quality
  • Periodic re-evaluation against the same fixed set
  • An incident path with rollback and a notification decision

Scope

Services included

  1. 01AI policy, standards and acceptable-use design
  2. 02Use-case intake, classification and approval workflows
  3. 03Model/vendor risk and due diligence
  4. 04Privacy, security and data protection by design
  5. 05Evaluation for quality, safety, bias and robustness
  6. 06Human oversight, auditability and incident response
  7. 07Regulatory readiness and evidence packs

Artifacts

Typical deliverables

  • AI policy and control framework
  • Use-case and model inventory
  • Risk-tiering method and approval workflow
  • Threat model and control requirements
  • Evaluation scorecards and release criteria
  • Audit trail and incident playbook

Deliverables are named artifacts: roadmaps, architectures, controls, training and runbooks. Not slideware.