AI Governance Operationalisation
Turn AI governance from policy into operational control.
Review and design how AI is actually approved, constrained, monitored, evidenced and escalated inside day-to-day workflows.
The problem
Governance that exists only in policy does not govern the operation.
Teams need to know what they may use, against which data and knowledge, under what authority, with what review, what evidence must be retained, when escalation is required and who remains accountable.
Scope
Scope.
- Use-case approval
- Human oversight
- Agent authority
- Approved tools, models and contexts
- Knowledge-source authority
- Data and confidentiality boundaries
- Evidence and logging
- Quality review
- Monitoring
- Incident and exception handling
- Change and version control
- Accountability
Outputs
Deliverables.
- Operational governance model
- Control and approval design
- Human and agent authority controls
- Evidence and audit requirements
- Monitoring and exception framework
- Source and knowledge-authority requirements
- Change-control model
- Implementation roadmap
Outcome
What the work is designed to make possible.
Enhancial does not provide legal certification or replace competent legal, regulatory or professional authority. The work operationalises governance requirements into systems and workflows.
AI initiatives should earn the right to scale.
Start from the evidence, authority and operating conditions around the work rather than from a preferred tool.