Policy Enforcement
The automated or manual mechanisms that ensure AI operations adhere to organizational policies, regulatory rules, and ethical guidelines.
The set of technical controls (e.g., policy-as-code gates, admission controllers, automated audits) and human-review processes that validate compliance at runtime and deployment. Policy enforcement tools intercept model builds, deployments, and API calls - checking against policy definitions (data usage rules, model-risk thresholds) and blocking or flagging any deviations for review.
A financial-services firm codifies its data-retention policy in a policy engine: any storage operation older than the TTL defined for PII fields is automatically purged. If a model-training job attempts to load wiped data, the engine rejects the job and notifies the data-governance team.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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