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AI Governance

Control without constraint

We build governance frameworks that balance innovation speed with risk discipline. Controls designed for regulators, workflows designed for operators.

Discuss Governance View Deliverables

The Challenge

Governance that enables, not blocks

Most AI governance fails because it's designed for compliance theatre, not operational reality. Controls that look good on paper become obstacles that teams work around.

The result: shadow AI, compliance gaps, and risk exposure that boards don't see until it's too late.

Common Tensions

  • Regulatory requirements vs. innovation velocity
  • Centralised control vs. business unit autonomy
  • Comprehensive oversight vs. operational friction
  • Risk elimination vs. competitive enablement

Our Approach

Governance designed for reality

We build frameworks that work in practice, not just in policy documents. Every control is tested against operational workflows before deployment.

1

Risk Landscape Mapping

We assess your AI risk exposure across regulatory, operational, reputational, and strategic dimensions.

2

Framework Design

We design governance structures that match your risk appetite, regulatory environment, and organisational culture.

3

Control Implementation

We embed controls into existing workflows, minimising friction while maximising oversight effectiveness.

4

Operationalisation

We train teams, establish monitoring mechanisms, and create continuous improvement loops.

Choose your starting point

AI Governance Modules

Governance modules you can deploy individually or combine for comprehensive AI risk management, each with a primary focus on Assurance or Enablement.

Assurance

AI Governance Framework

Guardrails that enable, not block, AI.

Purpose: To ensure AI is safe, controlled, explainable, and compliant — enabling innovation without increasing risk.

Protects you from: Uncontrolled adoption; Regulatory exposure; Model behaviour risk.

Best when: You need a governance backbone for safe, scalable AI.

Scope your Define your AI governance framework →
Assurance

AI Policy & Standards Suite

Plain-language rules everyone can follow.

Purpose: To define clear rules for safe and appropriate AI usage across the organisation.

Protects you from: Inconsistent AI use; Legal ambiguity; Team-level confusion.

Best when: You need clear rules people will actually follow.

Scope your Build your AI policy & standards suite →
Assurance

AI Controls Catalogue

Map every AI risk to a real control.

Purpose: To provide technical and operational controls that ensure AI behaves safely and predictably.

Protects you from: Security gaps; Unchecked outputs; Missing guardrails.

Best when: You need reliable controls before deploying at scale.

Scope your Catalogue your AI controls →
Assurance

Accountability & Decision Framework

No more "who signed off on this?"

Purpose: To clarify decision rights and responsibilities across the AI lifecycle.

Protects you from: Blurred ownership; Ambiguous decisions; Inter-team friction.

Best when: You need clarity on who’s accountable — and when.

Scope your Clarify AI accountability →
Assurance

Explainability & Auditability Framework

Every AI decision with a paper trail.

Purpose: To ensure AI decisions can be traced, justified, and defended.

Protects you from: Unexplainable decisions; Audit failure; Regulator scrutiny.

Best when: You need AI decisions you can defend under pressure.

Scope your Make AI explainable & audit-ready →
Assurance

Shadow AI Register

See every AI tool, not just the approved ones.

Purpose: To give visibility into AI tools, models, and usage across the organisation.

Protects you from: Hidden AI usage; Untracked risk; Compliance blind spots.

Best when: You need visibility before tightening controls.

Scope your shadow AI register →
Enablement

Sovereign AI Governance Architecture

Own your AI, don't rent your decision-making.

Purpose: To ensure organisations retain ownership, control, and accountability over AI inferences, behaviour, and institutional intelligence — regardless of vendor or platform.

Empowers you to: Maintain sovereign control over critical intelligence, inference origin, and model behaviour.

Best when: You need to own, not rent, your AI decision infrastructure.

Scope your Design your sovereign AI governance architecture →

What You Get

Deliverables designed for action

Each Governance Offering translates into concrete artefacts — frameworks, registers, catalogues and decision tools — that can be dropped into your existing control environment and used immediately by Risk, Compliance, and Technology.

Governance Framework

Complete structure with decision rights, oversight mechanisms, and accountability.

Policy Suite

AI-specific policies covering development, deployment, and operation.

Risk Register

Comprehensive AI risk inventory with assessment methodology and controls mapping.

Control Library

Documented controls with implementation guidance and effectiveness measures.

Compliance Matrix

Regulatory requirement mapping with control coverage and gap analysis.

Operating Model

Roles, responsibilities, and workflows for ongoing governance operation.

Expected Outcomes

What changes after we engage

Risk Visibility

Clear understanding of AI risk exposure with controls that address gaps.

Regulatory Confidence

Compliance posture that satisfies current requirements and adapts to change.

Operational Integration

Governance embedded in workflows, not bolted on as an afterthought.

Ready to build governance that works?

Start with a conversation about your risk landscape and regulatory requirements.

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