Operationalizing AI Governance at Runtime
CIOs and AI program leads asking how to move AI governance from policy documents into operational enforcement across all AI workflows.
Das Problem
Most enterprise AI governance exists as documents, frameworks, and review committees — not as operational controls. The gap between 'we have an AI governance policy' and 'that policy is enforced every time an AI agent acts' is the operationalization gap. Closing it requires three things: (1) policy logic expressed as machine-readable code, not documents; (2) a runtime that evaluates every AI action against that code before execution; and (3) an immutable audit log that proves enforcement happened. Corules provides all three. AI governance frameworks (NIST AI RMF, EU AI Act, ISO 42001) describe what governance should achieve. Corules is the operational layer that makes it happen at runtime.
So löst Corules es
Corules's policy runtime evaluates structured context against compiled CEL expressions — returning ALLOW, BLOCK, or ESCALATE with a reason and audit ID.
Richtlinienbeispiel
// Governance as operational code, not document:
// Instead of: "Discounts above 25% require VP approval" (policy doc)
// Implement: evaluated at every decision, before every execution
discount_pct <= params.max_discount_by_tier[context.customer_tier]
|| (discount_pct <= params.vp_override_ceiling && context.actor_role == "vp_sales")Frequently Asked Questions
How do we get governance from documents into code?
Policy authors translate governance requirements into CEL expressions. Corules provides a policy authoring workflow with compile-time validation — expressions that don't evaluate correctly are caught before they reach production.
Can we start with one workflow and expand?
Yes. Corules is deployed per use case. Start with your highest-risk AI workflow — discount approvals or credit decisions, for example — validate the enforcement model, then expand to other workflows using the same infrastructure.
How do we demonstrate AI governance compliance to regulators?
The audit ledger provides evidence: every AI decision, the policy version that governed it, the actor, and the outcome — queryable and exportable. Corules is designed to produce the evidence regulators and auditors ask for.