AI Governance
Practical control over how AI is used in your business
The problem is rarely a lack of policy. It is that nobody is fully sure how the AI is being used, who owns the risk, or what should happen when something goes wrong.
Accountability Chain
StableBusiness Owner / Accountable Lead
Ultimate accountability
Governance Owner
Oversight & approval
Data / Model / Risk Owners
Operational control
Audit & Compliance Review
Independent challenge
Accountability holds when ownership is named, decisions are traceable, and escalation routes are tested.
Documentation drift
AI policies are written once and then left behind. By the time an issue arises, the documented process no longer matches how the system is actually being used.
Diffuse accountability
Responsibility spreads across software, suppliers, and staff. When a decision is questioned, nobody can say clearly who owned it and who approved it.
Gaps discovered too late
Weak review, monitoring, and deployment oversight often surface only after a complaint, loss event, or legal challenge.
What strong governance looks like
Ownership, traceability, review, and deployment oversight
Governance that holds under pressure has three properties. Each is testable against actual operations — not policy documents.
Accountability
Named owners, documented approval paths, and escalation routes that work in practice when management needs answers.
Traceability
A reviewable chain from input to AI output to business action, including overrides, approvals, and changes.
Operational control
Release, monitoring, and review arrangements strong enough to support ongoing use rather than one-off compliance language.
Start the process
Understand and strengthen how your business controls AI
A short discussion is usually enough to identify where control is weakest and what needs to change first.