AI Governance
Organisations need a repeatable way to evaluate AI use cases, understand the data and platform involved, apply proportionate controls and make deliberate decisions without allowing AI adoption to become unmanaged Shadow IT.
Assessment Model
New AI capabilities will keep appearing. A durable governance model should work across platforms by asking the same core questions in a consistent order.
What problem is being solved and who owns the outcome?
What information will be received, processed, retained or exposed?
Is the service approved and are supplier, privacy and access risks understood?
Could failure, bias or misuse materially affect people or the organisation?
Are oversight, security, testing, monitoring and acceptable-use controls adequate?
Proceed, proceed with controls, assess further or do not proceed.
Reassess when the model, data, purpose, users or risk materially change.
Live Tools
The tools below are lightweight browser-based assessments designed to help teams structure early decisions before moving into detailed legal, privacy, security or risk review.
AI Governance · Live Assessment
Screens an AI use case across business purpose, data classification, platform status, impact, human oversight and core controls.
The result is a governance screening outcome, not an automated approval.
Responsible AI · Live Assessment
A broader readiness check covering governance, data, approved platforms, acceptable use, human oversight, testing, monitoring and Shadow AI.
Useful for assessing whether the organisation has the surrounding controls needed to scale AI safely.
Decision Outcomes
The objective is not to make every use case risk-free. It is to make the decision deliberate, proportionate, documented and owned.
Low-risk use case with adequate existing controls.
Acceptable once defined controls or conditions are implemented and owned.
Material uncertainty or elevated impact requires specialist review.
Current risk cannot be reduced to an acceptable level or conflicts with requirements.
Data + AI Governance
AI governance becomes more practical when ownership, quality, classification, access, lineage and lifecycle are already understood. Rather than building a separate governance silo, Responsible AI can extend the underlying data-governance model.
Governance Philosophy
The objective of governance should not be to become the department that says no to AI. It should provide enough structure that the organisation can reach a consistent and informed yes, no or yes-with-controls decision.
That means applying more governance where the consequences are higher, keeping low-risk experimentation proportionate, and maintaining clear ownership for the controls, exceptions and ongoing review that matter.