AI Governance & Policy Development
AI governance for operational decision systems
Organisations are deploying AI but lack clear policies on what's allowed, who approves what, how to handle incidents, and what data models can access. Legal and compliance teams block projects out of caution. Teams build AI tools without oversight. Risk is real but poorly managed.
AI Governance & Policy Development establishes practical guardrails - use case evaluation criteria, approval workflows, data access controls, incident response procedures, and escalation paths. This is operational policy that helps teams move forward safely, not academic frameworks that sit on shelves.
For organisations that need clear AI governance to enable deployment, not prevent it.
What you get
Operational AI governance framework with clear policies, approval workflows, and incident procedures. Our customers use this to accelerate AI deployment by giving teams clear boundaries and reducing legal/compliance friction.
- Timeline:
- 3-6 weeks
- Deliverable:
- AI governance policy documentation, use case evaluation framework, approval workflows, data access controls, incident response procedures, team training materials
How it works
Use Case Risk Assessment
Evaluate AI use cases by risk level, customer-facing vs internal, sensitive data vs general, high-stakes vs low-impact, to determine appropriate guardrails.
Approval Workflow Design
Define who approves what, which AI applications require legal review, security review, executive approval, and establish clear escalation paths.
Data Access Controls
Determine what data AI models can access, what requires special handling (PII, financial, health), and how to enforce boundaries technically and procedurally.
Incident Response Procedures
Establish what constitutes an AI incident (hallucinations affecting customers, data leakage, bias issues), who responds, and how to contain and communicate.
Policy Documentation & Training
Document policies clearly, train teams on what's allowed and what requires approval, create practical decision trees for common scenarios.
What's required
Legal, compliance, and security stakeholder involvement. Clarity on organisational risk tolerance. Examples of use cases under consideration (actual, not theoretical). Commitment to practical guardrails, not perfection.
Good governance enables AI deployment by providing clear boundaries, not blocking everything out of caution.
"The governance framework gave our legal team confidence to say yes. We deployed three AI tools within six months of establishing it."
General Counsel, Healthcare Technology (UK)
Common questions about governance services
Common questions about governance services
What does governance as a service include?
Governance includes defining ownership, controls and accountability around operational decisions supported by AI.
How is this applied in practice?
It is applied to specific decisions where ownership and outcomes need to be clearly structured and monitored.
Is this separate from delivery work?
No. Governance is integrated into how decisions are structured and improved rather than added afterwards.
What problems does governance solve?
It reduces inconsistency, unmanaged risk and unclear ownership in operational decision-making.
Explore AI governance.
Let's discuss how governance can accelerate your AI initiatives, not block them.
Explore AI governance