A minimum governance structure for verifiable ownership, sources, approvals and incident handling.
Governance as an operating system
Governance is not a document separate from the product. It is the responsibilities, controls and records that determine daily operation. It should be proportionate to risk and readable across business, technology and control teams.
Start with an inventory of use cases: purpose, owner, users, data, model, actions and potential impact.
Essential roles
The process owner owns outcomes, the data owner owns meaning and access, the technical owner owns the pipeline, and the risk owner owns thresholds. Users approve or manage exceptions. A RACI prevents responsibility from being assigned vaguely to the AI.
For external providers, clarify versions, data location, retention, subprocessors and incident handling.
Lifecycle controls
Before release, validate, test errors and obtain approval. In operation, monitor drift, quality, overrides, latency and availability. Changes to prompts, models, rules and sources require versioning and revalidation.
Fallbacks must be tested, not merely documented. A critical process should continue safely when the AI component is unavailable.
A useful audit trail
Record the version, relevant inputs, sources, output, controls, human decision and executed action. Avoid copying unnecessary personal data into logs. Auditability and minimization must be designed together.
Schedule periodic reviews and a suspension procedure. The ability to stop the system is part of governance.
Next step
Before choosing a tool, assess the process with the Process Readiness assessment. For complex initiatives, explore the AI Process Intelligence method and ControlRoom use cases.
Frequently asked questions
Who is responsible for an AI output?
The organization and process owners remain responsible for decisions and applied controls.
What should be versioned?
Models, prompts, rules, sources, thresholds and transformations that can change the output.