Granular Consent
A data-privacy approach allowing individuals to grant or deny specific permissions for each type of data use, enhancing transparency and control.
Moves beyond blanket “agree/disagree” terms by offering segmented consent options (e.g., location tracking for navigation yes/no; analytics data yes/no). Implemented via consent-management platforms, it requires dynamic consent capture, enforcement in data pipelines, and regular consent audits. Governance must maintain consent records immutable for audits and support easy revocation.
A fitness-tracker app prompts users separately for heart-rate sharing, geolocation, and health analytics. Users can toggle each permission in their settings, and the system enforces those choices at data-ingest points - providing clear audit logs of consent status for each data use.
“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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