Ownership
The clear assignment of responsibility and authority over AI assets - data, models, processes - to ensure accountability throughout the system lifecycle.
Governance practice that designates “model owners,” “data stewards,” and “pipeline operators” for each AI component. Owners are accountable for compliance (validations, monitoring), incident responses, and retirement decisions. Clear ownership prevents orphaned systems, enforces role-based access, and aligns resources for maintenance and audits.
A global retailer tags each AI model in its registry with an owner’s name and contact. When a compliance audit finds missing impact assessments, the governance office notifies the listed owner to remediate within two weeks, ensuring accountability and rapid resolution.
“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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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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