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.
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What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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How is Enzai different from other governance tools?
Can we start if we have no existing AI governance process?
Does AI governance slow down innovation?
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