AI governance for production workflows
Governance becomes effective when it is built into decisions, controls, observability, and escalation paths in the workflow.
Governance is not a document, it is a workflow element
Classical governance works through policies, reviews, and approvals. In AI workflows that is not enough. Effective governance is built into the workflow: which decision points require humans, which controls fire automatically, which logs are created, which escalations follow which path. Governance that does not live inside the workflow does not protect anyone.
Four building blocks of effective AI governance
First: documented decisions with rationale. Second: technical and procedural controls with clear ownership. Third: observability that surfaces drift, anomalies, and usage patterns. Fourth: escalations that follow defined triggers rather than gut feel.
What the EU AI Act and sector regulation actually require
The regulatory expectations are less abstract than often feared, but they demand substance. Risk assessment per use case, data documentation, human oversight, logging, and incident responsiveness. Cortaris translates those requirements into workflow-resident controls instead of building a parallel compliance world.
Governance does not slow innovation
It is the opposite. A clearly documented risk and control architecture enables faster decisions because stakeholders know what has been reviewed. Governance becomes an accelerator the moment it is anchored in the workflow.
How Cortaris builds governance in practice
We start with a risk assessment per workflow, derive controls along data flow and decisions, define observability at the level that captures drift, and build escalation paths that actually reach the right stakeholders. All without a separate compliance bureaucracy.
What an initial maturity view should show
Existing policies, available logging and observability capacity, regulatory pressure per business area, leadership risk appetite, and cultural expectations for control. From that picture we design governance that fits the organization rather than one that is theoretically correct.
Next step
Start with a clear, low-risk next step.
We assess which workflows are commercially relevant, technically feasible, and operationally realistic.